Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Fueled by breakthrough technology developments, the biological, biomedical, and
behavioral sciences are now collecting more data than ever before. There is a critical need …
behavioral sciences are now collecting more data than ever before. There is a critical need …
Machine learning in geo-and environmental sciences: From small to large scale
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
When and why PINNs fail to train: A neural tangent kernel perspective
Physics-informed neural networks (PINNs) have lately received great attention thanks to
their flexibility in tackling a wide range of forward and inverse problems involving partial …
their flexibility in tackling a wide range of forward and inverse problems involving partial …
DeepXDE: A deep learning library for solving differential equations
Deep learning has achieved remarkable success in diverse applications; however, its use in
solving partial differential equations (PDEs) has emerged only recently. Here, we present an …
solving partial differential equations (PDEs) has emerged only recently. Here, we present an …
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Recently, the advent of deep learning has spurred interest in the development of physics-
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and
temporally discretization of the governing equation using polynomials into a finite …
temporally discretization of the governing equation using polynomials into a finite …
A deep collocation method for the bending analysis of Kirchhoff plate
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Advances in computational science offer a principled pipeline for predictive modeling of
cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and …
cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and …