Stability selection enables robust learning of partial differential equations from limited noisy data. - Suryanarayana Maddu; Bevan L. Cheeseman; Ivo F. Sbalzarini; Christian L. Müller (CoRR, 2019)

Learning physically consistent mathematical models from data using group sparsity. - Suryanarayana Maddu; Bevan L. Cheeseman; Christian L. Müller; Ivo F. Sbalzarini (CoRR, 2020)

STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations. - Suryanarayana Maddu; Dominik Sturm; Bevan L. Cheeseman; Christian L. Müller; Ivo F. Sbalzarini (CoRR, 2021)

Data-driven modeling and simulation of spatiotemporal processes with a view toward applications in biology - Suryanarayana Maddu (2021)

Learning deterministic hydrodynamic equations from stochastic active particle dynamics. - Suryanarayana Maddu; Quentin Vagne; Ivo F. Sbalzarini (CoRR, 2022)

Inverse Dirichlet weighting enables reliable training of physics informed neural networks. - Suryanarayana Maddu; Dominik Sturm; Christian L. Müller; Ivo F. Sbalzarini (Mach. Learn. Sci. Technol., 2022)

Parallel Discrete Convolutions on Adaptive Particle Representations of Images. - Joel Jonsson; Bevan L. Cheeseman; Suryanarayana Maddu; Krzysztof Gonciarz; Ivo F. Sbalzarini (IEEE Trans. Image Process., 2022)

Learning locally dominant force balances in active particle systems. - Dominik Sturm; Suryanarayana Maddu; Ivo F. Sbalzarini (CoRR, 2023)

Stochastic force inference via density estimation. - Victor Chardès; Suryanarayana Maddu; Michael J. Shelley (CoRR, 2023)

Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems. - Sarah Perez 003; Suryanarayana Maddu; Ivo F. Sbalzarini; Philippe Poncet (J. Comput. Phys., 2023)

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. - Ruben Ohana; Michael McCabe; Lucas Meyer; Rudy Morel; Fruzsina J. Agocs; Miguel Beneitez; Marsha Berger; Blakesley Burkhart; Stuart B. Dalziel; Drummond B. Fielding; Daniel Fortunato; Jared A. Goldberg; et al. (NeurIPS, 2024)

Inferring biological processes with intrinsic noise from cross-sectional data. - Suryanarayana Maddu; Victor Chardès; Michael J. Shelley (CoRR, 2024)

Learning fast, accurate, and stable closures of a kinetic theory of an active fluid. - Suryanarayana Maddu; Scott Weady; Michael J. Shelley (J. Comput. Phys., 2024)

Inferring stochastic dynamics with growth from cross-sectional data. - Stephen Zhang; Suryanarayana Maddu; Xiaojie Qiu; Victor Chardès (CoRR, 2025)