Data, source code, and experimental pipelines for the paper “Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming,”

ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equations.

Simulate cells and particles

Finite volume solver for incompressible multiphase flows with surface tension. Foaming flows in complex geometries.

Computational Microfluidics

Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning

High-fidelity Navier-Stokes solver for incompressible flows around obstacles with distributed block-structured adaptively refined grids.