Like all good models, this page is iteratively updated. :)
We are an interdisciplinary research collective pioneering the convergence of physics-based modeling and scientific machine learning to tackle complex scientific and engineering challenges. “We don’t replace physics — we augment it with intelligent data-driven models.”
Our lab is driven by the vision of redefining scientific discovery by blending first-principles physics with data-driven intelligence. Our core research thrusts include:
- Hybrid Physics-AI Solvers:
Coupling neural operators with finite element methods to accelerate the time-to-solution as well as long rollouts for complex dynamics and multiscale modelin - Latent Representations for Physical Systems:
Developing latent neural operators to learn low-dimensional, physics-consistent embeddings for design and control. - Scientific Machine Learning (SciML):
Leveraging PINNs and physics-informed operator learning to accelerate discovery. - Mechanics Datasets:
Compiling a collection of open-access mechanics datasets to evaluate the performance of machine learning models. Each dataset is generated using the open-source finite element software FEniCS and released under the CC BY-SA 4.0 license. You are welcome to download and use them for any research or educational purpose!
You can find a comprehensive list of our publications on our website. We also upload all our presentations from conferences and seminars on our GitHub Repository.
If you are interested in our work, you may also be interested in the SciML webinar series JHU-IITD SMaRT that we are hosting in collaboration with Prof. Souvik Chakraborty and Prof. Rajdip Nayek from the Indian Institute of Technology Delhi.