Matthieu Darcy

I am a fourth year PhD student in the Computing and Mathematical Sciences Department at Caltech, working under the supervision of Houman Owhadi. I am broadly interested in scientific machine learning, specifically in learning and predicting stochastic (partial) differential equations and stochastic time-series. 

My current research includes

• Inference and Numerical Methods for Stochastic (Partial) Differential Equations.

• Operator Learning.

• Learning Dynamical Systems.

• Generative models for multiscale systems.

CV

Recent news:

• Our new preprint on "Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks" is now available the ArXiv.

• I gave a talk at CIRM ( in Marseille) and SciCADE (at NUS) on kernel methods and PINNS for rough partial differential equations (slides).

• I gave a talk at SIAM Uncertainty Quantification 2024 on kernel methods for rough partial differential equations (slides).

• I gave a talk at the International Conference: Differential Equations for Data Science on the inference of SDEs with kernel methods (slides)

• Our paper "Kernel methods are competitive for operator learning" is now out in the Journal of Computational Physics. 

• I gave a talk on "Kernel methods are competitive for Operator Learning" at the 10th International Congress on Industrial and Applied Mathematics - August 22nd 2023 (slides).

• I gave a talk in the DataSig Rough Path Interest Group on "Kernel methods are competitive for Operator Learning" - March 28th 2023

• Our paper on "One-shot learning of stochastic differential equations with data adapted kernels" was accepted in Physica D - November 2023.