Research Expertise and Interest

machine learning, geometric deep learning, differentiable physics, dynamical systems, numerical methods, computational geometry, optimization

Research Description

Aditi Krishnapriyan's research interests are focused on developing machine learning methods that are motivated by the opportunities and challenges in science and engineering, with particular interest in physics-inspired machine learning methods. Some of the areas of exploration include approaches to incorporate physical inductive biases into ML models to improve generalization, the advantages that ML can bring to classical physics-based numerical solvers (such as through end-to-end differentiable frameworks and implicit layers), and better learning strategies for distribution shifts in the physical sciences. Our foundational research is informed by and grounded in applications in physics, fluid and molecular dynamics, materials design, climate science, and other related areas. This work also includes interfacing with other fields including numerical methods, dynamical systems theory, quantum mechanical simulations, computational geometry, and optimization.