Efficient Parameter Sampling for Neural Network Construction. - Drimik Roy Chowdhury; Muhammad Firmansyah Kasim (CoRR, 2019)

Up to two billion times acceleration of scientific simulations with deep neural architecture search. - Muhammad Firmansyah Kasim; Duncan Watson-Parris; Lucia Deaconu; Sophy Oliver; Peter W. Hatfield; Dustin H. Froula; Gianluca Gregori; Matt Jarvis; Samar Khatiwala; Jun Korenaga; Jacob Topp-Mugglestone; Eleonora Viezzer; et al. (CoRR, 2020)

ξ-torch: differentiable scientific computing library. - Muhammad Firmansyah Kasim; Sam M. Vinko (CoRR, 2020)

Derivatives of partial eigendecomposition of a real symmetric matrix for degenerate cases. - Muhammad Firmansyah Kasim (CoRR, 2020)

Learning the exchange-correlation functional from nature with fully differentiable density functional theory. - Muhammad Firmansyah Kasim; Sam M. Vinko (CoRR, 2021)

DQC: a Python program package for Differentiable Quantum Chemistry. - Muhammad Firmansyah Kasim; Susi Lehtola; Sam M. Vinko (CoRR, 2021)

Reducing the Long Tail Losses in Scientific Emulations with Active Learning. - Yi Heng Lim; Muhammad Firmansyah Kasim (CoRR, 2021)

Constants of motion network. - Muhammad Firmansyah Kasim; Yi Heng Lim (NeurIPS, 2022)

Building high accuracy emulators for scientific simulations with deep neural architecture search. - Muhammad Firmansyah Kasim; Duncan Watson-Parris; Lucia Deaconu; Sophy Oliver; Peter W. Hatfield; Dustin H. Froula; Gianluca Gregori; Matt Jarvis; Samar Khatiwala; Jun Korenaga; Jacob Topp-Mugglestone; Eleonora Viezzer; et al. (Mach. Learn. Sci. Technol., 2022)

Unifying physical systems' inductive biases in neural ODE using dynamics constraints. - Yi Heng Lim; Muhammad Firmansyah Kasim (Trans. Mach. Learn. Res., 2023)

Parallelizing non-linear sequential models over the sequence length. - Yi Heng Lim; Qi Zhu; Joshua Selfridge; Muhammad Firmansyah Kasim (ICLR, 2024)