This document discusses the potential transition from climate models to mechanistic explanations in climate science. It argues that understanding climate change through mechanisms could provide several advantages over the current model-based approach, such as introducing new explanations, integrating causal stories, and facilitating communication. However, some challenges are also noted, such as the holistic nature of climate science and concerns about reductionism. The document explores topics like feedback mechanisms, mapping models to mechanisms, and assessing climate models based on their representation of mechanisms. Overall, it presents arguments both for and against adopting a more mechanistic view of climate science.
Unit 1 – Basics of Mechanics
Topics to be covered – unit1
Basic kinematic concepts and definitions
Degree of freedom & Mobility
Kutzbach criterion & Gruebler’s criterion
Grashof’s Law
Kinematic inversions of four-bar-chain and slider crank chains – Limit positions
Mechanical advantage – Transmission Angle
Classification of mechanisms
Description of some common mechanisms
Unit 1-introduction to Mechanisms, Kinematics of machines of VTU Syllabus prepared by Hareesha N Gowda, Asst. Prof, Dayananda Sagar College of Engg, Blore. Please write to hareeshang@gmail.com for suggestions and criticisms.
20.18 Optimization Problems In Air Pollution ModelingKelly Lipiec
This document discusses the use of optimization problems and adjoint equations in air pollution modeling. It notes that mathematical models are needed to design reliable control strategies to keep pollution levels under critical levels. Optimization is required to determine how and where to reduce emissions in an optimal way. The document outlines the formulation of air pollution models using systems of partial differential equations and describes how data assimilation can be used to obtain initial concentration fields and optimize model parameters, emissions, and deposition rates. It also discusses how adjoint equations and variational data assimilation have been successfully applied in meteorology to compute gradients and find optimal initial conditions.
This document provides an introduction to mathematical modeling. It discusses key concepts such as dimensional analysis, the Buckingham Pi theorem, types of mathematical models including static vs dynamic, discrete vs continuous, deterministic vs probabilistic, and linear vs nonlinear models. Examples of mathematical modeling applications in various fields like physics, engineering, biology and combat modeling are provided. The modeling process from defining the real-world problem to model formulation, solution, evaluation and refinement is outlined. Dimensional analysis using Rayleigh, Buckingham and Bridgman methods is explained through a heat transfer example.
A guide to molecular mechanics and quantum chemical calculationsSapna Jha
This document provides an introduction and guide to molecular mechanics and quantum chemical calculations. It is divided into four main sections. The first section defines various theoretical models used for quantum chemical and molecular mechanics calculations. The second section evaluates the performance of different theoretical models for predicting properties such as geometries, reaction energies, vibrational frequencies, and more. The third section discusses practical strategies for carrying out calculations. The fourth section presents case studies that illustrate how calculations can provide insight into chemistry. The guide aims to help chemists select appropriate computational methods for different applications.
Understanding climate model evaluation and validationPuneet Sharma
The document discusses model evaluation and validation. It introduces key concepts like evaluation, validation, and the apple-orange problem when directly comparing models and observations. It describes using a satellite simulator like COSP to facilitate apple-to-apple comparisons by simulating what satellites would observe from the model. The document also notes issues with observations like errors and uncertainties that must be considered during evaluation.
Theory and Applications of Monte Carlo Simulations by Chan V. (Ed.).pdfssuser941d48
This document discusses three tasks related to analyzing experimental data using Monte Carlo simulations:
1) Identifying a theoretical distribution that best fits multiple datasets by varying its parameters.
2) Testing the statistical significance between two empirical distributions from different datasets.
3) Testing the statistical significance between two fitted theoretical distributions of the same type from different datasets.
The document outlines statistical approaches for each task, including using information criteria to identify the best fitting distribution, the Kuiper two-sample test to compare empirical distributions, and bootstrap simulations to construct distributions for comparing fitted distributions when independence is violated. The methods are demonstrated on characterizing fibrin structure from scanning electron microscopy images.
Unit 1 – Basics of Mechanics
Topics to be covered – unit1
Basic kinematic concepts and definitions
Degree of freedom & Mobility
Kutzbach criterion & Gruebler’s criterion
Grashof’s Law
Kinematic inversions of four-bar-chain and slider crank chains – Limit positions
Mechanical advantage – Transmission Angle
Classification of mechanisms
Description of some common mechanisms
Unit 1-introduction to Mechanisms, Kinematics of machines of VTU Syllabus prepared by Hareesha N Gowda, Asst. Prof, Dayananda Sagar College of Engg, Blore. Please write to hareeshang@gmail.com for suggestions and criticisms.
20.18 Optimization Problems In Air Pollution ModelingKelly Lipiec
This document discusses the use of optimization problems and adjoint equations in air pollution modeling. It notes that mathematical models are needed to design reliable control strategies to keep pollution levels under critical levels. Optimization is required to determine how and where to reduce emissions in an optimal way. The document outlines the formulation of air pollution models using systems of partial differential equations and describes how data assimilation can be used to obtain initial concentration fields and optimize model parameters, emissions, and deposition rates. It also discusses how adjoint equations and variational data assimilation have been successfully applied in meteorology to compute gradients and find optimal initial conditions.
This document provides an introduction to mathematical modeling. It discusses key concepts such as dimensional analysis, the Buckingham Pi theorem, types of mathematical models including static vs dynamic, discrete vs continuous, deterministic vs probabilistic, and linear vs nonlinear models. Examples of mathematical modeling applications in various fields like physics, engineering, biology and combat modeling are provided. The modeling process from defining the real-world problem to model formulation, solution, evaluation and refinement is outlined. Dimensional analysis using Rayleigh, Buckingham and Bridgman methods is explained through a heat transfer example.
A guide to molecular mechanics and quantum chemical calculationsSapna Jha
This document provides an introduction and guide to molecular mechanics and quantum chemical calculations. It is divided into four main sections. The first section defines various theoretical models used for quantum chemical and molecular mechanics calculations. The second section evaluates the performance of different theoretical models for predicting properties such as geometries, reaction energies, vibrational frequencies, and more. The third section discusses practical strategies for carrying out calculations. The fourth section presents case studies that illustrate how calculations can provide insight into chemistry. The guide aims to help chemists select appropriate computational methods for different applications.
Understanding climate model evaluation and validationPuneet Sharma
The document discusses model evaluation and validation. It introduces key concepts like evaluation, validation, and the apple-orange problem when directly comparing models and observations. It describes using a satellite simulator like COSP to facilitate apple-to-apple comparisons by simulating what satellites would observe from the model. The document also notes issues with observations like errors and uncertainties that must be considered during evaluation.
Theory and Applications of Monte Carlo Simulations by Chan V. (Ed.).pdfssuser941d48
This document discusses three tasks related to analyzing experimental data using Monte Carlo simulations:
1) Identifying a theoretical distribution that best fits multiple datasets by varying its parameters.
2) Testing the statistical significance between two empirical distributions from different datasets.
3) Testing the statistical significance between two fitted theoretical distributions of the same type from different datasets.
The document outlines statistical approaches for each task, including using information criteria to identify the best fitting distribution, the Kuiper two-sample test to compare empirical distributions, and bootstrap simulations to construct distributions for comparing fitted distributions when independence is violated. The methods are demonstrated on characterizing fibrin structure from scanning electron microscopy images.
The document discusses process modeling and provides an overview of key concepts. It defines what a model is and different types of models including physical, mathematical, conceptual, black box, white box, and grey box models. It explains the purposes of modeling and discusses good modeling practices like parsimony, modesty, accuracy, and testability. The document also covers model development approaches, classification, errors, application areas, and strengths and weaknesses of different modeling techniques.
The document provides an overview of the Thermodynamics-I course offered by the Department of Mechanical & Manufacturing Engineering at MIT Manipal. The course covers fundamental concepts like system and surroundings, properties and states, the three statements of thermodynamics, and applications to pure substances and ideal gases. It aims to help students understand basic thermodynamic principles and apply them to analyze systems like heat engines, refrigerators, and other engineering applications involving energy transfer.
Advances in-the-theory-of-control-signals-and-systems-with-physical-modeling-...Nick Carter
This document provides an overview and summary of a book containing articles presented at three workshops on advances in control, signals, and systems theory, with an emphasis on physical modeling. The workshops covered electrical and mechatronic systems, mathematical tools, and chemical processes and life sciences. The book is organized into three parts corresponding to the workshops. It presents key contributions and surveys on topics like modeling of multi-body mechanical systems, sliding mode control, Hamiltonian modeling, nonlinear parameter estimation, underactuated mechanical systems, motion planning for pendulums, mechanical suspension systems, MEMS modeling and control, flatness characterization, nonholonomic mechanics, controlled Lagrangians, input delay compensation, genetic regulatory networks, cancer modeling, ventilation modeling,
This document summarizes a paper that argues optimization is an outdated paradigm in computer-aided engineering (CAE). Stochastic simulation is proposed as a better alternative that can account for uncertainty and complexity in a more complete way. Optimization focuses too much on numerical details rather than physics. Nature produces robust, "good enough" designs through self-organization and emergence rather than optimization. Stochastic simulation using Monte Carlo techniques can replace optimization by evaluating design performance across many random samples to find robust, high-performing designs rather than strictly optimal ones. This paradigm of stochastic simulation and design improvement is argued to provide a simpler and more effective approach for engineering problems.
This document discusses mechanisms in science. It begins by outlining different definitions of mechanisms provided by Machamer, Darden and Craver, Glennan, and Bechtel and Abrahamsen. It then discusses a possible consensus definition provided by Illari and Williamson. The document outlines why mechanisms are important in explaining causal processes in fields like biology, neuroscience, and social science. It discusses how mechanisms are used in explanation, the relationship between mechanisms and functions, and how evidence of mechanisms can be used in causal assessment.
Complex numbers have various applications in mechanical engineering, including air foil design, control theory, quantum mechanics, and relativity. In air foil design, the Joukowsky transform uses complex analysis to model air flow around a foil by distorting it from potential flow around a cylinder. Control theory involves using complex numbers to represent rotations and model systems like cruise control. Quantum mechanics formulations make use of complex wave functions and Hilbert spaces. Relativity formulas sometimes use imaginary time to simplify metrics in spacetime.
(E book) thermodynamics fundamentals for applications - j. o'connell, j. ha...Christianne Cristaldo
The document is the preface to a textbook on thermodynamics fundamentals for applications. It provides brief biographies of the authors J.P. O'Connell and J.M. Haile, acknowledges contributions, and outlines the content covered in the textbook over its four parts:
1. The basics of thermodynamics including primitive concepts, the first and second laws, and fundamental relations.
2. Properties of single-phase systems relative to ideal gases and ideal solutions, as well as equations of state and excess properties.
3. Multiphase and reacting systems including phase equilibria, criteria for phase stability, and phase diagrams for real systems.
4. Engineering calculations for
Optimal Control: Perspectives from the Variational Principles of Mechanicsismail_hameduddin
Optimal control is a tremendously important and popular area of research in modern control engineering. The extraordinary elegance of optimal control results, the significance of their implications and the unresolved nature of their practical implementation have excited the minds of generations of engineers and mathematicians. Despite the ongoing interest in optimal control, an appreciation of the origins of optimal control in analytical mechanics is still lacking. This work overviews the fundamentals of optimal control and attempts to expose the deeper connections between optimal control and early results in analytical mechanics. The two-point boundary value problem is given due importance (with its parallel in analytical mechanics) and special emphasis is placed on the feedback form of optimal control (Hamilton-Jacobi-Bellman equation) since this ties in closely with Hamilton-Jacobi theory of analytical mechanics. Numerical solutions to the optimal control problem and in particular, the generalized Hamilton-Jacobi-Bellman equation with successive Galerkin approximations, are also discussed to highlight recent trends and motivations behind optimal control research.
This thesis examines calculations of binding free energies between cucurbit[7]uril and small ligands using end-state molecular modeling methods. The author conducted molecular dynamics simulations using AMBER with implicit solvent models to calculate binding free energies, which were then compared to experimental values from the SAMPL4 challenge. Various error metrics including R2 correlation, root mean squared error, and linear regression slope were used to evaluate the results. The author found some improvement over previous methods, suggesting further refinement of conformational sampling could enhance the accuracy of binding free energy predictions.
This document evaluates the MATLAB toolbox MATCONT for constructing bifurcation diagrams of chemical process systems. MATCONT is a relatively new software that allows for the continuation of static and dynamic equilibria of nonlinear systems. The document demonstrates MATCONT's capabilities using a well-studied example of a nonlinear ethanol fermentation process that exhibits rich dynamic behavior including multiplicity, oscillations, and chaos. The document concludes that MATCONT is a robust, flexible, and user-friendly tool recommended for bifurcation analysis of nonlinear systems in both research and teaching.
This document summarizes a presentation on stochastic turbulence modeling using smoothed particle hydrodynamics. It discusses why turbulence is an important topic across many fields, provides an overview of fundamental concepts in turbulence modeling from Reynolds to Kolmogorov, and describes the statistical and stochastic point of view taken in turbulence modeling, including the use of Fokker-Planck and BBGKY equations. It also discusses applications of turbulence modeling concepts in engineering, science, and theoretical assumptions underlying probabilistic density function methods.
How to use data to design and optimize reaction? A quick introduction to work...Ichigaku Takigawa
(Journal Club) ICReDD Seminar, Apr 27 2020
Institute for Chemical Reaction Design and Discovery (ICReDD)
Hokkaido University
Sapporo, JAPAN
https://www.icredd.hokudai.ac.jp
This document provides an introduction to the concepts behind stochastic models, estimation, and control. It discusses the need for stochastic approaches due to uncertainties in system models, disturbances beyond our control, and noise-corrupted sensor data. The optimal estimator for linear systems driven by white Gaussian noise, the Kalman filter, provides an estimate that minimizes the statistical error. It processes all available measurements recursively by propagating the conditional probability density of the state variables given the measurements. The Kalman filter combines system models, noise statistics, and measurements to generate the best estimate.
This document summarizes previous numerical studies of detonation waves in premixed hydrogen-air mixtures and assesses the accuracy of simplified chemical reaction mechanisms. It describes the classical ZND detonation wave structure and compares numerical results to ZND theory. Several chemical mechanisms of varying complexity are evaluated using zero-dimensional combustion simulations and one-dimensional detonation wave simulations. Comparisons to experimental detonation speeds help validate the chemical models. The document discusses limitations of previous studies and aims to better understand the effects of chemical kinetics models on computed detonation wave structures.
The document discusses modeling turbulent premixed and partially premixed combustion. It notes that directly solving transport equations for all reactive species moments is impractical due to the large number of equations and closure terms required. Instead, models track the first two moments of one or two key scalars like mixture fraction and progress variable. The document reviews modeling approaches for lean premixed flames and describes the strained flamelet model and its extension to partially premixed flames. It discusses implementing the model in CFD codes and comparing results.
Analysis of Existing Models in Relation to the Problems of Mass Exchange betw...YogeshIJTSRD
The main recommendations of this article mainly analyzing the rate of harmful elements the period of exploitation of the automobile implements and its services to develop activity of automobile implements of the exploitation period. Shavkat Giyazov "Analysis of Existing Models in Relation to the Problems of Mass Exchange between Autotransport Complex and the Environment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38681.pdf Paper URL: https://www.ijtsrd.com/engineering/automotive-engineering/38681/analysis-of-existing-models-in-relation-to-the-problems-of-mass-exchange-between-autotransport-complex-and-the-environment/shavkat-giyazov
This document discusses models used in climate science and uncertainty quantification. It begins by introducing the types of models, including general circulation models (GCMs) that simulate the climate system. A key point is that climate models provide probability distributions of weather rather than single predictions. The document emphasizes that uncertainty quantification is essential in climate science given the complexity of the climate system and imperfections in both models and observations. It presents a Bayesian framework for combining information from multiple models and data sources to obtain probability distributions of climate projections and quantify associated uncertainties. Gaussian process emulation is discussed as a method for approximating computationally expensive climate models to facilitate Bayesian calibration and inference.
Virtue in Machine Ethics: An Approach Based on Evolutionary Computation Ioan Muntean
February 2015. Co-author: Don Howard, University of Notre Dame). Presented at the American Philosophical Association (APA Central). St. Louis, Missouri.
The document discusses process modeling and provides an overview of key concepts. It defines what a model is and different types of models including physical, mathematical, conceptual, black box, white box, and grey box models. It explains the purposes of modeling and discusses good modeling practices like parsimony, modesty, accuracy, and testability. The document also covers model development approaches, classification, errors, application areas, and strengths and weaknesses of different modeling techniques.
The document provides an overview of the Thermodynamics-I course offered by the Department of Mechanical & Manufacturing Engineering at MIT Manipal. The course covers fundamental concepts like system and surroundings, properties and states, the three statements of thermodynamics, and applications to pure substances and ideal gases. It aims to help students understand basic thermodynamic principles and apply them to analyze systems like heat engines, refrigerators, and other engineering applications involving energy transfer.
Advances in-the-theory-of-control-signals-and-systems-with-physical-modeling-...Nick Carter
This document provides an overview and summary of a book containing articles presented at three workshops on advances in control, signals, and systems theory, with an emphasis on physical modeling. The workshops covered electrical and mechatronic systems, mathematical tools, and chemical processes and life sciences. The book is organized into three parts corresponding to the workshops. It presents key contributions and surveys on topics like modeling of multi-body mechanical systems, sliding mode control, Hamiltonian modeling, nonlinear parameter estimation, underactuated mechanical systems, motion planning for pendulums, mechanical suspension systems, MEMS modeling and control, flatness characterization, nonholonomic mechanics, controlled Lagrangians, input delay compensation, genetic regulatory networks, cancer modeling, ventilation modeling,
This document summarizes a paper that argues optimization is an outdated paradigm in computer-aided engineering (CAE). Stochastic simulation is proposed as a better alternative that can account for uncertainty and complexity in a more complete way. Optimization focuses too much on numerical details rather than physics. Nature produces robust, "good enough" designs through self-organization and emergence rather than optimization. Stochastic simulation using Monte Carlo techniques can replace optimization by evaluating design performance across many random samples to find robust, high-performing designs rather than strictly optimal ones. This paradigm of stochastic simulation and design improvement is argued to provide a simpler and more effective approach for engineering problems.
This document discusses mechanisms in science. It begins by outlining different definitions of mechanisms provided by Machamer, Darden and Craver, Glennan, and Bechtel and Abrahamsen. It then discusses a possible consensus definition provided by Illari and Williamson. The document outlines why mechanisms are important in explaining causal processes in fields like biology, neuroscience, and social science. It discusses how mechanisms are used in explanation, the relationship between mechanisms and functions, and how evidence of mechanisms can be used in causal assessment.
Complex numbers have various applications in mechanical engineering, including air foil design, control theory, quantum mechanics, and relativity. In air foil design, the Joukowsky transform uses complex analysis to model air flow around a foil by distorting it from potential flow around a cylinder. Control theory involves using complex numbers to represent rotations and model systems like cruise control. Quantum mechanics formulations make use of complex wave functions and Hilbert spaces. Relativity formulas sometimes use imaginary time to simplify metrics in spacetime.
(E book) thermodynamics fundamentals for applications - j. o'connell, j. ha...Christianne Cristaldo
The document is the preface to a textbook on thermodynamics fundamentals for applications. It provides brief biographies of the authors J.P. O'Connell and J.M. Haile, acknowledges contributions, and outlines the content covered in the textbook over its four parts:
1. The basics of thermodynamics including primitive concepts, the first and second laws, and fundamental relations.
2. Properties of single-phase systems relative to ideal gases and ideal solutions, as well as equations of state and excess properties.
3. Multiphase and reacting systems including phase equilibria, criteria for phase stability, and phase diagrams for real systems.
4. Engineering calculations for
Optimal Control: Perspectives from the Variational Principles of Mechanicsismail_hameduddin
Optimal control is a tremendously important and popular area of research in modern control engineering. The extraordinary elegance of optimal control results, the significance of their implications and the unresolved nature of their practical implementation have excited the minds of generations of engineers and mathematicians. Despite the ongoing interest in optimal control, an appreciation of the origins of optimal control in analytical mechanics is still lacking. This work overviews the fundamentals of optimal control and attempts to expose the deeper connections between optimal control and early results in analytical mechanics. The two-point boundary value problem is given due importance (with its parallel in analytical mechanics) and special emphasis is placed on the feedback form of optimal control (Hamilton-Jacobi-Bellman equation) since this ties in closely with Hamilton-Jacobi theory of analytical mechanics. Numerical solutions to the optimal control problem and in particular, the generalized Hamilton-Jacobi-Bellman equation with successive Galerkin approximations, are also discussed to highlight recent trends and motivations behind optimal control research.
This thesis examines calculations of binding free energies between cucurbit[7]uril and small ligands using end-state molecular modeling methods. The author conducted molecular dynamics simulations using AMBER with implicit solvent models to calculate binding free energies, which were then compared to experimental values from the SAMPL4 challenge. Various error metrics including R2 correlation, root mean squared error, and linear regression slope were used to evaluate the results. The author found some improvement over previous methods, suggesting further refinement of conformational sampling could enhance the accuracy of binding free energy predictions.
This document evaluates the MATLAB toolbox MATCONT for constructing bifurcation diagrams of chemical process systems. MATCONT is a relatively new software that allows for the continuation of static and dynamic equilibria of nonlinear systems. The document demonstrates MATCONT's capabilities using a well-studied example of a nonlinear ethanol fermentation process that exhibits rich dynamic behavior including multiplicity, oscillations, and chaos. The document concludes that MATCONT is a robust, flexible, and user-friendly tool recommended for bifurcation analysis of nonlinear systems in both research and teaching.
This document summarizes a presentation on stochastic turbulence modeling using smoothed particle hydrodynamics. It discusses why turbulence is an important topic across many fields, provides an overview of fundamental concepts in turbulence modeling from Reynolds to Kolmogorov, and describes the statistical and stochastic point of view taken in turbulence modeling, including the use of Fokker-Planck and BBGKY equations. It also discusses applications of turbulence modeling concepts in engineering, science, and theoretical assumptions underlying probabilistic density function methods.
How to use data to design and optimize reaction? A quick introduction to work...Ichigaku Takigawa
(Journal Club) ICReDD Seminar, Apr 27 2020
Institute for Chemical Reaction Design and Discovery (ICReDD)
Hokkaido University
Sapporo, JAPAN
https://www.icredd.hokudai.ac.jp
This document provides an introduction to the concepts behind stochastic models, estimation, and control. It discusses the need for stochastic approaches due to uncertainties in system models, disturbances beyond our control, and noise-corrupted sensor data. The optimal estimator for linear systems driven by white Gaussian noise, the Kalman filter, provides an estimate that minimizes the statistical error. It processes all available measurements recursively by propagating the conditional probability density of the state variables given the measurements. The Kalman filter combines system models, noise statistics, and measurements to generate the best estimate.
This document summarizes previous numerical studies of detonation waves in premixed hydrogen-air mixtures and assesses the accuracy of simplified chemical reaction mechanisms. It describes the classical ZND detonation wave structure and compares numerical results to ZND theory. Several chemical mechanisms of varying complexity are evaluated using zero-dimensional combustion simulations and one-dimensional detonation wave simulations. Comparisons to experimental detonation speeds help validate the chemical models. The document discusses limitations of previous studies and aims to better understand the effects of chemical kinetics models on computed detonation wave structures.
The document discusses modeling turbulent premixed and partially premixed combustion. It notes that directly solving transport equations for all reactive species moments is impractical due to the large number of equations and closure terms required. Instead, models track the first two moments of one or two key scalars like mixture fraction and progress variable. The document reviews modeling approaches for lean premixed flames and describes the strained flamelet model and its extension to partially premixed flames. It discusses implementing the model in CFD codes and comparing results.
Analysis of Existing Models in Relation to the Problems of Mass Exchange betw...YogeshIJTSRD
The main recommendations of this article mainly analyzing the rate of harmful elements the period of exploitation of the automobile implements and its services to develop activity of automobile implements of the exploitation period. Shavkat Giyazov "Analysis of Existing Models in Relation to the Problems of Mass Exchange between Autotransport Complex and the Environment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38681.pdf Paper URL: https://www.ijtsrd.com/engineering/automotive-engineering/38681/analysis-of-existing-models-in-relation-to-the-problems-of-mass-exchange-between-autotransport-complex-and-the-environment/shavkat-giyazov
This document discusses models used in climate science and uncertainty quantification. It begins by introducing the types of models, including general circulation models (GCMs) that simulate the climate system. A key point is that climate models provide probability distributions of weather rather than single predictions. The document emphasizes that uncertainty quantification is essential in climate science given the complexity of the climate system and imperfections in both models and observations. It presents a Bayesian framework for combining information from multiple models and data sources to obtain probability distributions of climate projections and quantify associated uncertainties. Gaussian process emulation is discussed as a method for approximating computationally expensive climate models to facilitate Bayesian calibration and inference.
Similar to 2014 10 rotman mecnhanism and climate models (20)
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The document discusses two approaches to analyzing quantum gravity (QG) programs from a philosophy of science perspective: 1) Philosophical centrism, where QG programs are analyzed for what they say about philosophical concepts like space, time, and objects, and 2) Philosophical analysis, where QG programs are evaluated as scientific theories or programs. It focuses on string theory as a case study, exploring how string theory makes claims about metaphysical questions and can be analyzed philosophically as a scientific endeavor. Key questions discussed include which QG program best addresses a given metaphysical question and which has virtues or drawbacks as a scientific program.
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2012 09 duality and ontic structural realism bristolIoan Muntean
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
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Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
1. FROM MODELS TO MECHANISMS.
FEEDBACK AND OPTIMIZATION IN
CMIP5
IOAN MUNTEAN
THE REILLY CENTER FOR SCIENCE, TECHNOLOGY, AND VALUES
UNIVERSITY OF NOTRE DAME
IMUNTEAN@ND.EDU
1
2. PREVIEW
Main issue: A transition from models to mechanisms in climate change
Argument for a mechanistic view in climate change
Feedback
Optimality
Control/ manipulation
Understanding
Arguments against mechanisms in climate science
Holism
Failed mechanisms in physical sciences
So what?
Not yet
Will never happen
Not needed
2
3. WHAT IS UNDER SCRUTINY HERE?
The internal structure of climate models
Feedback in climate models
Mapping models to mechanism (M2M)
Many to one?
Many to many?
Optimality and plurality of models
Communicating results, metadata and mechanisms
3
4. SOME TOPICS OF INTEREST IN CLIMATE
SCIENCE
Social values (viz. Epistemic values) in creating models (Winsberg 2012)
Complexity of models and “analytic understanding” (Parker 2014)
Multiplicity/plurality of climate models, (Parker 2010a)
Uncertainty of models
Stability, reliability of models
Explanatory power and understanding of climate models
Modularity
Adapted from (Knutti and Sedláček 2012)
4
5. ROLES OF MODELS
Climate change is mainly about building and assessing models
Climate models are mainly:
predicting tools
generate other models or hypotheses
Quantification of theories of climate change
“hybrid”: predict and explain
Do climate models really explain? How?
Do we have an IBE with climate models?
5
6. OVERLAPPING MODELS IN MECHANISMS
Differenct communities focus on different parts
They do not necessarily look at the “coupled model”
Climate scientists are specialized
6
7. SOME VIEWS ABOUT MODELS AND
MECHANISMS
Models are not related to mechanisms
mathematical models exist in physics, without being related to any mechanism
some models summarize data (phenomenal models)
some other models predict (are phenomenally adequate) but do not explain
Models represent mechanisms
One task of model building is to represent the dynamics of mechanisms (Bechtel and
Abrahamsen 2011)
Models needs mechanisms to be explanatory
Models are explanatory when they describe a mechanism (Craver 2006)
Models map to mechanisms (M2M)
Let us call these models “mechanistic models”
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8. MODEL ASSESSMENT IN CLIMATE SCIENCE
Confirmation of “the truth” of existing models (Lloyd 2010)
Adequacy-for-a-purpose: (Parker 2009)
Realism: accurate description of the actual climate system
Bayesian view
Possibilism (Katzav 2014)
Present focus: mapping models to a mechanism
How does model X map on the mechanism Y?
8
9. THREE EXTREME PREDICTIONS
A. Where do I need to look in the sky to find the moon in London ON
at 16:30 on 25.10.2044?
B. What will be Ioan’s state of health on 4:30 on 25.10.2044, given this
and this constraints on the world and what we know of his diet,
genes, etc.?
C. How far can I drive a Honda Civic car from London ON with a full
tank of gas, in this direction, in the weather conditions, all things
being equal?
A= one theory, simple simulation, simple data, perfect prediction
B= no theory no model, some mechanisms
C= one mechanism, no theory, some initial conditions, no need of
models
9
10. QUESTIONS
1 Are some (all?) climate models mechanistic?
2 Why explanation?
3 Can climate models explain without being “mechanistic”?
What advantages does a mechanistic view bring to climate science?
4 So what? Why do we need mechanistic explanation anyway?
10
1 yes, those in which feedback plays a role
2 We want to understand the “causal story” of the climate system. The understanding of why a
phenomenon occurs (Parker 2014).
Question to Parker: is a mechanistic explanation better than causal explanation in improving our
understanding of a phenomenon and of its question “why?”
3 yes, they can, but still mechanistic explanations can do better
4 Because with explanation, control, understanding and manipulation come!
4’ we can hope for the optimal model
11. EXTRAPOLATING MECHANISMS
Universality: Model-building occurs anywhere in science
Neuroscience/cognitive science (empirical data and laws/equations)
Biology (empirical data)
Physics (laws, symmetries)
Life science, medicine
Scientific revolution can be read, charitably as a process building models,
mechanisms, unifying, eliminating models, creating theories etc.
I think it makes sense to talk about:
“mechanisms & models (together) in climate science”
11
12. A SIMPLIFIED VIEW
I. Convergence from a plurality of models to a limited number of models
Culling models
Coupling submodels
Constraint models
II. Mapping from a limited number of models to a limited number of mechanisms
III. Convergence of mechanisms to a theory (unification of mechanisms and
models)
I think II deserves attention in the light of CMIP5
I am quietist about III. And I is already discussed in the literature
12
13. ADVANTAGES OF MECHANISMS
Introduce new explanations
Integrate causal stories
Introduce levels
Facilitate communication between submodels and between subroutines
Can map elements of models to mechanisms and give them materiality
Cluster of different models into mechanisms based on the M2M
Move from statistical explanations/arguments close to what the layman wants to
hear (not probabilities, but conditionals)
13
14. “BOTTOMING OUT” MECHANISMS
Ignore the fundamental and fundamentality (deep physics)
Work at scales
Relative to a scale (time space energy)
Multiscale modeling
14
15. FROM MODELS TO MECHANISMS
Why do we need mechanisms? A Kantian innuendo:
“Dynamical models without mechanistic grounding are empty, while mechanistic
models without complex dynamics are blind.” (Bechtel and Abrahamsen 2011)
This suggests a relation among models and mechanisms.
Normatively: models and mechanism should be mapped one onto the other.
15
16. DO MODELS EXPLAIN?
The Craver-Kaplan hypothesis (Kaplan and Craver 2011):
Models explain only when there is a model-to-mechanism mapping. M2M
Models needs to be modular in order to explain (Weber 2008)
16
17. THE MECHANISM-MODEL MAPPING
Biologists discover mechanisms
Models resemble the mechanism
Some models are better, some are worse, in representing the mechanism
17
18. MECHANISMS IN MODELS’ CLOTHING
Are mechanisms already in the climate science?
Try to identify in CMIP-5 the mechanistic mindset (not language)
Unveil their explanatory role
Explain the M2M mapping.
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19. SYNTHETIC MODELING
Mechanism complements the computational modeling
It is not a question of reinterpretation of what climate scientists
already do
It is more or less a reconstruction based on M2M
It does bring in a clearly stated language of levels
Cycles of amplification are called amplifying mechanisms
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20. MECHANISTIC OPERATIONS IN THE MODELS
Decomposition is a procedure that happens in mechanisms
Switch on and off various components:
Inhibition
Stimulation
Recomposition of the operation of the mechanisms (Bechtel, 2011)
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21. CLIMATE FEEDBACK 101
Feedback is never linear
Apply a forcing (CO2)
Temperature raise
Feedback changes
Look for mechanisms that are not switched off al low temperature
Once these processes go on, there is amplification or reducing of the temperature
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22. FEEDBACK
Feedback can be positive or negative
The net feedback from the combined effect of changes in water vapor, and
differences between atmospheric and surface warming is almost surely positive.
The net radiative feedback due to all cloud types combined is positive.
22
23. CMIP-5: A LOLLAPALOOZA OF FEEDBACK
AOGCM are not enough!
Earth System Models
Earth System Models of Intermediate Complexity
Includes cycles
Since AR4, the understanding of mechanisms and feedbacks of extreme in
temperature improved
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25. FEEDBACK: M2M
Feedback can be captured by:
Non-linear equations
A cycle in a mechanism
Simple mechanisms are serial: start to finish. They contain only linear causal chains
Feedback loops complicate mechanisms.
They are non-sequential
Introduce timescale
Synchronization of feedback (makes them positive or negative, depending on phase
factor)
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27. CARBON CYCLE IN CMIP5 AND FEEDBACK
Increased atmospheric CO2 increases land and ocean uptake
Limitations on plant growth imposed by nitrogen availability
27
28. VAPOUR-CO2-CLIMATE
Vapour is a feedback not a Forcing of climate change
It is a fast and strong feedback (see Ch 8)
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29. HOW DO WE REACH OPTIMALITY?
Optimality does not belong to a model
Through mechanisms (Machamer, Darden, and Craver 2000)
Optimal mappings between models and mechanisms
Reduce uncertainty
29
30. TIMESCALE MATTERS!
The effect of feedbacks is clear for longer timespans
Some feedbacks are delayed by centuries or millennia
30
31. Lifetime (years) GWP20 GWP100 GTP20 GTP100
31
CH b 4
12.4a Nocc fb 84 28 67 4
With cc fb 86 34 70 11
HFC-134a 13.4 Nocc fb 3710 1300 3050 201
With cc fb 3790 1550 3170 530
CFC-11 45.0 Nocc fb 6900 4660 6890 2340
With cc fb 7020 5350 7080 3490
N2O 121.0a Nocc fb 264 265 277 234
With cc fb 268 298 284 297
CF4 50,000.0 Nocc fb 4880 6630 5270 8040
With cc fb 4950 7350 5400 9560
32. ARGUMENTS AGAINST M2M IN CLIMATE
SCIENCE Climate science is a physical science in which mechanisms do not
play the same role as in neuroscience/life science/
Some “disastrous” examples of mechanism thought in physics (ether,
phlogiston, Cartesian physics)
Climate models are mathematical models, unlike models in
neuroscience
Climate science is holistic, in pursue of complexity, not reductionist.
Emergence looms large
Climate science is more about statistical reasoning, not about
discovering reality/mechanisms.
Climate modelers are partially blackboxing, or probably grey-boxing
their object of study
32