Familiar techniques of separation of variables and Fourier series can be used to solve a variety of pde based on domains in the plane, however these techniques do not extend naturally to surface problems. Instead we look to take a computational approach. The talk will cover the basics of finite difference and finite element approximations of the one dimensional heat equation and show how to extend these ideas on to surfaces. If time allows, we will show numerical results of an optimal partition problem based on a sphere. No background knowledge of pde or computation is required.
This presentation gives the basic idea about the methods of solving ODEs
The methods like variation of parameters, undetermined coefficient method, 1/f(D) method, Particular integral and complimentary functions of an ODE
This presentation gives the basic idea about the methods of solving ODEs
The methods like variation of parameters, undetermined coefficient method, 1/f(D) method, Particular integral and complimentary functions of an ODE
This presentation gives example of "Calculus of Variations" problems that can be solved analytical. "Calculus of Variations" presentation is prerequisite to this one.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
3 examples of PDE, for Laplace, Diffusion of Heat and Wave function. A brief definition of Fouriers Series. Slides created and compiled using LaTeX, beamer package.
Mathematical description of Legendre Functions.
Presentation at Undergraduate in Science (math, physics, engineering) level.
Please send any comments or suggestions to improve to solo.hermelin@gmail.com.
More presentations can be found on my website at http://www.solohermelin.com.
Dyadics algebra.
Please send comments and suggestions to solo.hermelin@gmail.com. Thanks.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
This presentation gives example of "Calculus of Variations" problems that can be solved analytical. "Calculus of Variations" presentation is prerequisite to this one.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
3 examples of PDE, for Laplace, Diffusion of Heat and Wave function. A brief definition of Fouriers Series. Slides created and compiled using LaTeX, beamer package.
Mathematical description of Legendre Functions.
Presentation at Undergraduate in Science (math, physics, engineering) level.
Please send any comments or suggestions to improve to solo.hermelin@gmail.com.
More presentations can be found on my website at http://www.solohermelin.com.
Dyadics algebra.
Please send comments and suggestions to solo.hermelin@gmail.com. Thanks.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
The following presentation is a part of the level 4 module -- Electrical and Electronic Principles. This resources is a part of the 2009/2010 Engineering (foundation degree, BEng and HN) courses from University of Wales Newport (course codes H101, H691, H620, HH37 and 001H). This resource is a part of the core modules for the full time 1st year undergraduate programme.
The BEng & Foundation Degrees and HNC/D in Engineering are designed to meet the needs of employers by placing the emphasis on the theoretical, practical and vocational aspects of engineering within the workplace and beyond. Engineering is becoming more high profile, and therefore more in demand as a skill set, in today’s high-tech world. This course has been designed to provide you with knowledge, skills and practical experience encountered in everyday engineering environments.
New Mathematical Tools for the Financial SectorSSA KPI
AACIMP 2010 Summer School lecture by Gerhard Wilhelm Weber. "Applied Mathematics" stream. "Modern Operational Research and Its Mathematical Methods with a Focus on Financial Mathematics" course. Part 5.
More info at http://summerschool.ssa.org.ua
Jordan-Lie algebra of single-spin chiral fieldsSelim Gómez
An intriguing possibility for going beyond the Standard Model is extending the matter spectrum through the addition of fundamental high spin fields. Here we describe a study of the (j,0)⊕(0,j) family of Lorentz algebra representations where we built a covariant basis for the operators on these spaces. These operators generate an algebraic structure additional to the symmetry algebra, analogous to the Clifford algebra satisfied by the Dirac gamma matrices. This structure, which mathematicians call a Jordan-Lie algebra, is spin-dependent. The construction is based on an analysis of the covariant properties of the parity operator, which for these representations transforms as the completely temporal component of a symmetrical tensor of rank 2j. We make the construction explicit for j=1/2,1 and 3/2, and provide an algorithm for the corresponding calculations for arbitrary j.
My paper for Domain Decomposition Conference in Strobl, Austria, 2005Alexander Litvinenko
We did a first step in solving, so-called, skin problem. We developed an efficient H-matrix preconditioner to solve diffusion problem with jumping coefficients
An Asymptotic Approach of The Crack Extension In Linear PiezoelectricityIRJESJOURNAL
Abstract: As a result of a theoretical technique for elucidating the fracture mechanics of piezoelectric materials, this paper provides, on the basis of the three-dimensional model of thin plates, an asymptotic behavior in the Griffith’s criterion for a weakly anisotropic thin plate with symmetry of order six, through a mathematical analysis of perturbations due to the presence of a crack. It is particularly established, in this work, the effects of both electric field and singularity of the in-plane mechanical displacement on the piezoelectric energy
An Approach to Analyze Non-linear Dynamics of Mass Transport during Manufactu...BRNSS Publication Hub
In this paper, we introduce an approach to increase integration rate of elements of a hybrid comparator with the first dynamic amplifying stage and the second quasi-dynamic latching stage. Framework the approach, we consider a heterostructure with special configuration. Several specific areas of the heterostructure should be doped by diffusion or ion implantation. Annealing of dopant and/or radiation defects should be optimized
On Decreasing of Dimensions of Field-Effect Transistors with Several Sourcesmsejjournal
We analyzed mass and heat transport during manufacturing field-effect heterotransistors with several
sources to decrease their dimensions. Framework the result of manufacturing it is necessary to manufacture
heterostructure with specific configuration. After that it is necessary to dope required areas of the heterostructure by diffusion or ion implantation to manufacture the required type of conductivity (p or n). After
the doping it is necessary to do optimize annealing. We introduce an analytical approach to prognosis mass
and heat transport during technological processes. Using the approach leads to take into account nonlinearity of mass and heat transport and variation in space and time (at one time) physical parameters of these
processes
El 7 de noviembre de 2016, la Fundación Ramón Areces organizó el Simposio Internacional 'Solitón: un concepto con extraordinaria diversidad de aplicaciones inter, trans, y multidisciplinares. Desde el mundo macroscópico al nanoscópico'.
Neutral Electronic Excitations: a Many-body approach to the optical absorptio...Claudio Attaccalite
Neutral Electronic Excitations: a Many-body approach to the optical absorption spectra.
Introduction to Bethe-Salpeter equation and linear response theory.
Similar to How to Solve a Partial Differential Equation on a surface (20)
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
How to Solve a Partial Differential Equation on a surface
1. How to Solve a Partial Differential Equation on a
Surface
Tom Ranner
University of Warwick
T.Ranner@warwick.ac.uk
http://go.warwick.ac.uk/tranner
University of Warwick, Graduate Seminar,
3rd November 2010
2. Where do surface partial differential equations come from?
Partial differential equations on surfaces occur naturally in many
different applications for example:
fluid dynamics,
materials science,
cell biology,
mathematical imaging,
several others. . .
4. Example Surfaces – Pattern Formation
Invited Article R253
Figure 1. Examples of self-organization in biology. Clockwise from top-left: feather bud
patterning, somite formation, jaguar coat markings, digit patterning. The first is reprinted from
Widelitz et al 2000, β-catenin in epithelial morphogenesis: conversion of part of avian foot scales
into feather buds with a mutated β-catenin Dev. Biol. 219 98–114, with permission from Elsevier.
The second is courtesy of the Pourqui´ Laboratory, Stowers Institute for Medical Research, and
e
the remainder are taken from the public image reference library at http://www.morguefile.com/.
(a) Activator (b) Inhibitor
80 80 8
15
Taken from R E Baker, E A Gaffney and P K Maini, Partial differential equations for self-organisation in cellular
60 60
Distance (y)
Distance (y)
and development biology. Nonlinearity 21 (2008) R251–R290.
40 10 40 4
20 20
5. Example Surfaces – Dealloyed surface
The surface can be the interface of two materials in an alloy.
9738 C. Eilks, C.M. Elliott / Journal of Computational Physics 227 (2008) 9727–9741
Fig. 3. Simulation on a large square, t ¼ 0:04; t ¼ 0:1 and t ¼ 0:2.
Taken from C The geometric motion ofNumerical simulation of dealloying by surface nonvanishing right hand evolving surface
plane. Eilks, C M Elliott, the surface has little influence, except for providing for a dissolution via the side for the
conservation law of gold on the surface, since gold from the bulk is accumulating on the surface. While the concrete
finite element method. Journal of Computational Physics 227 random distribution in the bulk, the lengthscales of the
appearance of the structure obviously depends on the particular (2008) 9727–9741.
structure depend only on the particular values of the parameters in the equation.
After the phases have separated, etching still continues in the areas with a small concentration of gold, while the motion
is negligible in regions covered by gold yielding a maze like structure of the surface. The origins of this shape can still be
explained by the initial phase separation which fixed the gold covered regions that proceeded to move into the bulk. So
at this stage the simulation does not necessarily show the mechanisms for the emergence of a nanoporous structure. By
undercutting the gold rich portion of the surface, the area of the surface that is not covered by gold increases. In the last
stages of this simulation new components of the gold rich phase emerge at the bottom of the surface. Additionally the inter-
face separating gold rich and gold poor phases shows no effect of coarsening as for the planar Cahn-Hilliard equation, but
instead becomes more complicated. These two effects can be seen as signs that the model shows increasing formation of
morphological complexity. We explore them in more detail in the following examples. Note however that due to self-inter-
sections the surface is not embedded at later stages, as can be seen in Fig. 4, where the cross sections along the plane parallel
6. 1D Heat Equation
The one dimensional heat equation is given by
ut = uxx on (0, 1) × (0, T )
u(0, t) = u(1, t) = 0 for t ∈ (0, T )
u(x, t) = u0 (x) for x ∈ (0, 1).
Joseph Fourier solved this in 1822 using separation of variables.
The idea is to write
u(x, t) = X (x)T (t)
and derive a system of decoupled odes for X and T , which can
then be solved simply.
7. 2D Heat Equations
In two dimensions the heat equation on a square
Ω = (0, 1) × (0, 1) becomes
ut = ∆u := uxx + uyy in Ω × (0, T )
u = 0 on ∂Ω × [0, T ]
u(x, 0) = u0 (x) on Ω × {0}.
This can be solved using Fourier analysis. We rewrite
u(x, t) = uk (t)e ik·x ,
ˆ
k∈Z2
and derive a system of odes of uk .
ˆ
8. What is the surface Heat Equation?
For a d-dimensional hypersurface Γ we would like to write down
something like ut = ∆u but u is only define on Γ. So instead we
have
ut = ∆Γ u,
where ∆Γ is the Laplace-Beltrami operator.
9. Surface gradients
For a function η : Γ → R we define the surface gradient Γη
of η by
Γη = η − (ν · η)
where ν is the outward pointing normal on Γ and η is the
gradient in ambient coordinates.
10. Surface gradients
For a function η : Γ → R we define the surface gradient Γη
of η by
Γη = η − (ν · η)
where ν is the outward pointing normal on Γ and η is the
gradient in ambient coordinates.
The Laplace-Beltrami operator ∆Γ is given by the surface
divergence of the surface gradient
∆Γ η = Γ · Γ η.
11. Surface Heat Equation
The surface heat equation on a closed surface Γ is given by
ut = ∆Γ u on Γ × [0, T ]
u(x, 0) = u(x) on Γ.
12. Surface Heat Equation
The surface heat equation on a closed surface Γ is given by
ut = ∆Γ u on Γ × [0, T ]
u(x, 0) = u(x) on Γ.
How do we solve this equation?
separation of variables
Fourier analysis
parametrisation
approximation. . .
13. Approximation of the 1D Heat Equation – using finite
differences
Let’s first go back to the 1D problem is demonstrate some possible
methods. Take the 1D heat equation
ut = uxx ,
and let’s approximate the derivatives by finite differences. We
divide (0, 1) into N intervals of length ∆x, (xj , xj+1 ) for
j = 0, . . . N. Our approximate solution u h solves for
j = 1, . . . , N − 1 and t ∈ (0, T )
h u h (xj−1 , t) − 2u h (xj , t) + u h (xj+1 , t)
ut (xj , t) = .
∆x 2
14. Approximation of the 1D Heat Equation – using finite
differences
From, for j = 1, . . . , N − 1 and t ∈ (0, T )
h u h (xj−1 , t) − 2u h (xj , t) + u h (xj+1 , t)
ut (xj , t) = ,
∆x 2
we wish to discretize in time. We approximate the time derivative
on the left hand side in the same way, but there is a choice on the
right-hand side to evaluate at t = tk or tk+1 . We choose for
numerical reasons the Backwards Euler method t = tk+1 . So we
have a linear system
u h (xj , tk+1 ) − u h (xj , tk ) u h (xj−1 , tk+1 ) − 2u h (xj , tk+1 ) + u h (xj+1 , tk+1
=
∆t ∆x 2
15. Approximation of the 1D Heat Equation – using finite
differences
The solve strategy is then
1. Initialise u h (xj , 0) = u0 (xj ) for j = 0, . . . , N
2. For k = 0, 1, 2, . . . solve the linear system
∆t h
u h (xj , tk+1 ) − u (xj−1 , tk+1 ) − 2u h (xj , tk+1 ) + u h (xj+1 , tk+1 )
∆x 2
= u h (xj , tk )
for j = 1, . . . , N − 1, and
u h (xj , tk+1 ) = 0
for j = 0, N.
16. Approximation of the 1D Heat Equation – using finite
differences
This was implemented in MATLAB with the following result:
17. Approximation of the 2D Heat Equation – using finite
differences
The same method can be implemented for the 2D problem, with
the following result:
18. Can finite differences work on a surface?
This method works best on a regular grid, which is almost
always impossible on a surface.
One must parameterise the surface first!
Projections or embeddings can be used to solve a the surface
pde using this type of method. An example of this type of
method is the closest point method.
Instead, we can try to approximate the solution rather than
the problem.
19. Approximation of the 1D Heat Equation – using finite
elements
Again we split the domain (0, 1) into N intervals of length h. We
define the space Vh of finite element functions to be
Vh = {ηh ∈ C0 (0, 1) : ηh |(xj ,xj+1 ) is affine linear for each j}.
We would like to solve
h h
ut = uxx
so that u h (·, t) ∈ Vh for all t, but the finite element functions
don’t have two derivatives in space!
20. Approximation of the 1D Heat Equation – using finite
elements
The finite element functions do have a first derivative almost
everywhere, so we put the equations in integral form to remove
one of the derivatives.
21. Approximation of the 1D Heat Equation – using finite
elements
The finite element functions do have a first derivative almost
everywhere, so we put the equations in integral form to remove
one of the derivatives. We multiply by a test function φ and
integrate with respect to x
1 1
ut φ = uxx φ,
0 0
we then integrate by parts using the boundary condition
u(0) = u(1) = 0 to get
1 1
ut φ + ux φ x = 0
0 0
Now all the terms in the above equation exist for all
1
u, φ ∈ Vh ⊆ H0 (0, 1). This is called the weak form of the heat
equation.
22. Approximation of the 1D Heat Equation – using finite
elements
We wish to find u h (·, t) ∈ Vh such that for all time t
1 1
h h
ut φ + ux φx = 0.
0 0
We would like this to be true for all φ ∈ Vh , but this is
equivalent to being true for all basis functions φj ∈ Vh .
23. Approximation of the 1D Heat Equation – using finite
elements
We wish to find u h (·, t) ∈ Vh such that for all time t
1 1
h h
ut φ + ux φx = 0.
0 0
We would like this to be true for all φ ∈ Vh , but this is
equivalent to being true for all basis functions φj ∈ Vh .
We can find a basis for Vh by setting φj (xi ) = δij . Our
problem is to find u h (·, t) ∈ Vh for t such that
1 1
h h
ut φ j + ux (φj )x = 0 for j = 1, . . . , N.
0 0
Notice that the boundary condition is automatically satisfied if
u h ∈ Vh .
24. Approximation of the 1D Heat Equation – using finite
elements
We can decompose u h in terms of the basis function φj to get
N
u h (x, t) = Ui (t)φj (x).
i=0
The equations become
1 1
Ui,t φi φj + Ui (φi )x (φj )x = 0,
0 i 0 i
If we write U(t) = (U0 (t), . . . , UN (t)) and define the mass matrix
M and stiffness matrix S by
1 1
Mij = φi φj Sij (φi )x (φj )x ,
0 0
we can write a matrix system
MUt = SU.
25. Approximation of the 1D Heat Equation – using finite
elements
We discretize in time using backwards Euler again to get the
following solve strategy:
1. Initialise U 0 as Uj0 = u0 (xj ).
2. For each k = 0, . . ., solve the linear system
(M + ∆tS)U k+1 = MU k .
26. Approximation of the 1D Heat Equation – using finite
elements
This method was implemented in MATLAB with the following
result:
27. Approximation of the 2D Heat Equation – using finite
elements
This method can also be used for the 2D problem:
28. tinuously; the method should also be robust enough to tolerate different resolutions
and boundaries; for database indexing, each class index should be small for storage
Can we use finite elements for surface partial differential
and easy to compute.
Conformal mapping has many nice properties to make it suitable for classification
problems. Conformal mapping only depends on the Riemann metric and is indepen-
equations? dent of triangulation. Conformal mapping is continuously dependent of Riemann
metric, so it works well for different resolutions. Conformal invariants can be repre-
sented as a complex matrix, which can be easily stored and compared. We propose to
use conformal structures to classify general surfaces. For each conformally equivalent
class, we can define canonical parametrization for the purpose of comparison.
Geometry matching can be formulated to find an isometry between two surfaces.
We can use what is called a surface finite element method, in
By computing conformal parametrization, the isometry can be obtained easily. For
which we approximate the domain Γ also.
surfaces with close metrics, conformal parametrization can also give the best geometric
matching results.
Fig. 1. Surface & mesh with 20000 faces
1.1. Preliminaries. In this section, we give a brief summary of concepts and
Justification of this method including well posedness, stability and convergence can be found in G. Dziuk and C. M.
notations.
Elliott, Surface finite elements for parabolic equations. J.realization |K| is homeomorphic
Let K be a simplicial complex whose topological Comput. Math. 25(4), (2007) 385–407. Image taken
from Xianfeng Gucompact 2-dimensional manifold. SupposeConformal Structures of surfaces. Communications in
to a and Shing-Tung Yau Computing there is a piecewise linear embedding
Information and Systems. 2(2) (2002), 121–146.
(1) F : |K| → R3 .
The pair (K, F ) is called a triangular mesh and we denote it as M . The q-cells of K
29. Solving the surface heat equation – using surface finite
elements
We define Γh to be a polyhedral approximation of Γ (made of
triangles) with vertices xi .
Vh is the space of piecewise linear functions on Γh with basis
φj given by φj (xi ) = δij
We look to solve the weak form of the surface heat equation
on Γh :
h h
ut φ + Γh u · Γh φ = 0.
Γh Γh
30. Solving the surface heat equation – using surface finite
elements
We define the surface mass matrix M and surface stiffness
matrix S by
Mij = φi φj Sij = Γ h φi · Γ h φj
Γh Γh
Using the same notation for U as before we have
MUt + SU = 0.
31. Solving the surface heat equation – using surface finite
elements
We discretize using backwards Euler to get the solve strategy
1. Initialise U 0 by Uj0 = u0 (xj )
2. For k = 0, 1, . . ., solve the linear system
(M + ∆tS)U k+1 = MU k .
32. Solving the surface heat equation – using surface finite
elements
This method was implemented using the ALBERTA finite element
toolbox on S 2 to get:
33. Optimal Partition Problem
Given a surface Γ, a positive integer m and parameter ε > 0, we
wish to minimised the following energy functional for a
vector-valued function u = {uj } ∈ (H 1 (Γ))m :
2
E ε (u) = (| Γ u| + 2Fε (u)),
Γ
where Fε is in interaction potential of the form
m
1
Fε (u) = 2 ui2 + uj2 .
ε
i=1 j<i
Based on Quang Du and Fangda Lin, Numerical approximations of a norm-preserving gradinet flow and
applications to an optimal partition problem Nonlinearity 22 (2009) 67–83.
34. Optimal Partition Problem – Gradient Decsent
If we minimised E ε subject to uj L2 (Γ) = 1 for each j we have the
following norm preserving gradient decent equations:
uj,t − ∆Γ uj = λj (t)uj − Fε,uj (u) on Γ × R+
|uj |2 = 1 for j = 1, . . . , m.
Γ
along with the initial condition
u(0, x) = g (x) ∈ H 1 (Γ, Ξ), with gj L2 (Γ) = 1.
Here Ξ is the singular subset in Rm
m
Ξ = y ∈ Rm : yj2 yk = 0, yk ≥ 0 for 1 ≤ k ≤ m .
2
j=1 k<j
35. Optimal Partition Problem – Partition?
Since the vector-valued function u takes values on in Ξ, the above
system is equivalent to the following problem
For a given surface Γ, partition Γ into m region Γj such
that the sum j λj , with λj the first eigenvalue of ∆Γ
over Γj with a Dirichlet boundary condition, minimised.
36. Optimal Partition Problem – How to solve?
To progress from step tn to tn+1 , given u n we calculate u n+1 by
1. Set u n+1 as the solution the heat equation at t = tn+1
ˆ
ut = ∆Γ u for x ∈ Γ, tn < t < tn+1
u(tn , x) = u n (x).
2. use (Gauss-Seidel) solver of decoupled ODEs: sequentially for
j = 1. . . . , m,
2uj
uj,t = − 2 (˜in+1 )2 +
u (ˆin+1 )2 for x ∈ Γ, tn < t < tn+1
u
ε
i<j i>j
uj (tn , x) = ujn+1 (x)
ˆ
Set u n+1 as the solution u(tn+1 , x) of this system at t = tn+1 ,
˜
3. normalisation: we set u n+1 via
ujn+1
˜
ujn+1 = for j = 1, . . . , m,
ujn+1
˜
L2 (Γ)
37. Optimal Partition Problem – Numerical Results
This equation was discretize using a surface finite element method
and solved here for m = 6 on S 2 .
38. Summary
Methods such as Fourier series and separation of variables
don’t work on general surface so we must approximate
solutions.
Finite difference methods work by approximating the
differential operator by difference quotients, but only work
best on rectangular domains.
Finite element methods approximate weak solutions by
dividing up the domain into finitely many subdomains and can
be extended to surface problems.
A surface finite element method can be used to solve many
different types of problems on surfaces.