The document discusses scientific machine learning using SCIML, emphasizing its applications in physical systems and the need for open-source tools. It highlights advancements in simulations through techniques like physics-informed neural networks and neural differential equations, as well as the benefits of using Julia for speed and Python-like syntax. Additionally, it presents SCIML as a comprehensive software suite for modeling, analysis, and machine learning.

1. Scientific Machine Learning
using SciML
Aniket Kulkarni

2. Aniket Kulkarni
● MSc in Computational Fluid Dynamics from
Cranfield University
● Presales & application development @ Ansys
● Lead Data Scientist @ Aligned Automation
About Me
lnk.bio/ani.kulkarni

3. Physical Systems
System
Modules/
subsystems/
components
Design Aspects Electrical
Mechanical
Molecular
Fluid Dynamics

4. Virtual Modelling
● What is virtual modelling?
○ Wave equation :
○ Heat equation :
○ Existing tools - Matlab, Ansys etc.
● The need
○ Cost reduction
○ Visualization
● Need for open source tools

5. SciML
● Julia language
○ Speed
○ Python-like

6. SciML
● Software suite for scientific machine learning
○ Modelling
○ Solvers
○ Analysis
○ Machine Learning
○ Developer Tools

7. SciML Tools Walkthrough

8. Radioactive Decay of Carbon-14

9. Scientific Machine Learning
● Physics Informed Neural Networks (PINN)
● Neural Differential Equations
● Surrogates
● Inverse modelling
● GPU support

10. Physics Informed Neural Network

11. Physics Informed Neural Network

12. Summary
● Role of simulations in physical systems
● Advancements in simulations using Machine Learning
● Open source software in simulations
● SciML tools for scientific machine learning

13. Thank You