96 citations · 117 across the 4 of their papers we have counts for
4 papers
Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates
Yunfei Huang, David S. Greenberg
Neural PDE surrogates can improve the cost-accuracy tradeoff of classical solvers, but often generalize poorly to new initial conditions and accumulate errors over time. Physical a…
Sparse inference and active learning of stochastic differential equations from data
Yunfei Huang, Youssef Mabrouk, Gerhard Gompper +1
Automatic machine learning of empirical models from experimental data has recently become possible as a result of increased availability of computational power and dedicated algori…
A Bayesian traction force microscopy method with automated denoising in a user-friendly software package
Yunfei Huang, Gerhard Gompper, Benedikt Sabass
Adherent biological cells generate traction forces on a substrate that play a central role for migration, mechanosensing, differentiation, and collective behavior. The established…
Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells
Yunfei Huang, Christoph Schell, Tobias B. Huber +5
Adherent cells exert traction forces on to their environment, which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures. This tractio…