Neural operators for accelerating scientific simulations and design
Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …
physical experiments. Numerical simulations are an alternative approach but are usually …
Laplace neural operator for solving differential equations
Neural operators map multiple functions to different functions, possibly in different spaces,
unlike standard neural networks. Hence, neural operators allow the solution of parametric …
unlike standard neural networks. Hence, neural operators allow the solution of parametric …
Pde-refiner: Achieving accurate long rollouts with neural pde solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in science and
engineering. Recently, mostly due to the high computational cost of traditional solution …
engineering. Recently, mostly due to the high computational cost of traditional solution …
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Climatelearn: Benchmarking machine learning for weather and climate modeling
Modeling weather and climate is an essential endeavor to understand the near-and long-
term impacts of climate change, as well as to inform technology and policymaking for …
term impacts of climate change, as well as to inform technology and policymaking for …
A unified framework for U-Net design and analysis
U-Nets are a go-to neural architecture across numerous tasks for continuous signals on a
square such as images and Partial Differential Equations (PDE), however their design and …
square such as images and Partial Differential Equations (PDE), however their design and …
Scalable transformer for pde surrogate modeling
Transformer has shown state-of-the-art performance on various applications and has
recently emerged as a promising tool for surrogate modeling of partial differential equations …
recently emerged as a promising tool for surrogate modeling of partial differential equations …