Siddhartha Srivastava

Assistant Research Scientist, Mechanical Engineering

Sid Srivastava


My research broadly revolves around extending, specializing, and developing novel ML/AI methods for computational mechanics. My primary focus is data-driven physics-based modeling that utilizes approaches like Variational System Identification and PDE-constrained optimization. I apply these methods for inferring PDE models for complex physical phenomena, for instance, foldings during brain growth, deformation mechanics in soft matter (human tissue and ligaments), and migration and proliferation in biological cells. I also develop graph-based approaches for Machine Learning and NISQ (Noisy Intermediate Scale Quantum) computing. These methods are rooted in classical physics and mathematical analysis but simultaneously developed in concert with real-life experimental data.

Research Areas

Graph-theoretic algorithms
Machine Learning
Mechanics and Dynamics
Post-Moore computation
Scientific learning methods