Why SciML is the best way to transition to ML from any traditional domain?
Learn about our live upcoming course.
Imagine you are a mechanical engineer or economist or astrophysicist. You would like to capitalize on AI and incorporate it into your work.
Firstly, why is this helpful? Because there are domain experts and there are ML expert. But how often do you find a person who is very good at say structural engineering + machine learning? This is where Scientific Machine Learning enters the picture.
The problem with traditional way of learning ML
This is what most people do.
Step 1: Learn ML Theory
Step 2: Do toy Kaggle projects
Step 3: Update your resume claiming you are good at ML
I don’t have any problem with Step 1. It is a sensible thing to do. But Step 2 and Step 3 are the problems.
Projects done in traditional ML courses
Tell me how are these projects relevant to say a mechanical/electrical/civil engineer?
Automatic movie analyzer
MNIST digit recognizer
Movie recommendation systems
Titanic surviver prediction
These can be called as “Toy Kaggle Projects”.
You can play with toy Kaggle projects. But if you want to compete, you have to do serious ML projects. It was good to work on these projects to show expertise in 2012-2014. But not anymore!
We need the following kind of projects
Have a look at the below projects. They are serious ML projects that combine domain specific knowledge with ML. These are research papers published and accepted at top journals/conferences.
If you learn to do these projects on your own…
Is ML only meant for CS students? Absolutely not.
I had no background in formal CS
I did not take Data Structures, Algorithms courses
I did not take any CS course in undergraduate
I was a core mechanical engineer
ML is becoming more and more democratized. You don't need a formal CS degree to have a career in ML.
Scientific Machine Learning: Domains who can transition
Here are some domains from where you will find SciML engineers and scientists.
Mechanical
Electrical
Chemical
Biological
Physics
Economics
Data scientists
Geologists
Nuclear engineers
CFD/Fluid mechanics
Computer Science
Civil
Finance
Urban planning
Battery engineer
Bioengineer
Automation
Acturial Science
Robotics
Introduction to Scientific ML
Scientific Machine Learning (SciML) is the branch of ML which combines ML techniques with domain knowledge. In SciML, we can combine the power of neural networks with the interpretability of scientific structures like differential equations.
Any system that can be represented using an Ordinary or Partial Differential Equation is a potential candidate to setup Scientific ML problem.
Simple neural networks have the potential to learn wide variety of functions. Refer to Universal Approximation Theorem.
We combine the power of mechanistic models to be interpretable and the power of neural networks to be universal function approximators in SciML.
A bit about me
Starting from this January 23rd, 2025, I will be teaching Scientific ML live through our online course. If you are interested to join you can register here. We will close the registrations in a few days: Registration link.
Here is what we will be covering in the course
Course introduction
Transitioning to ML
Introduction to SciML
What is Julia?
Installing and understanding Julia programming language
Basic Julia programming
Differential equations
Introduction to differential equations
ODEs
PDEs
Differential equations in Julia
Running ODEs in Julia
Running PDEs in Julia
Solving ODEs in Julia
Solving PDEs in Julia
Introduction to Neural Networks
Feedforward neural networks
Gradient descent introduction
Backpropagation
The 3 pillars of Scientific ML
PINNs
Physics Informed Neural Networks (PINNs) - theory
Physics Informed Neural Networks (PINNs) - practical
Neural ODEs
Neural ODE - theory
Neural ODE - practical
Universal Differential Equation
UDE - theory
UDE - practical
Specific examples from each categories
1D wave equation simulation using PINN
Building Neural ODE in Julia
Modeling UDE in Julia
Hands-on research projects
Covid-19 infection prediction using SciML
Black hole dynamics using SciML
Modeling EV battery degradation using SciML
Chandrasekhar white dwarf equation modeling using SciML
How to write a research paper?
Idea to results
Results to story
Story to paper
Next step: SciML research
Potential topics
Top journals
Top conferences
What is the pre-requisite requirement to pursue the course?