Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
N McGreivy, A Hakim - Nature Machine Intelligence, 2024 - nature.com
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 …
[HTML][HTML] Machine Learning to speed up Computational Fluid Dynamics engineering simulations for built environments: A review
Computational fluid dynamics (CFD) represents a valuable tool in the design process of built
environments, enhancing the comfort, health, energy efficiency, and safety of indoor and …
environments, enhancing the comfort, health, energy efficiency, and safety of indoor and …
Physics-aware machine learning revolutionizes scientific paradigm for machine learning and process-based hydrology
Accurate hydrological understanding and water cycle prediction are crucial for addressing
scientific and societal challenges associated with the management of water resources …
scientific and societal challenges associated with the management of water resources …
Synergistic learning with multi-task deeponet for efficient pde problem solving
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful
information from multiple tasks to improve generalization performance compared to single …
information from multiple tasks to improve generalization performance compared to single …
Ditto: Diffusion-inspired temporal transformer operator
Solving partial differential equations (PDEs) using a data-driven approach has become
increasingly common. The recent development of the operator learning paradigm has …
increasingly common. The recent development of the operator learning paradigm has …
MyCrunchGPT: A chatGPT assisted framework for scientific machine learning
Scientific Machine Learning (SciML) has advanced recently across many different areas in
computational science and engineering. The objective is to integrate data and physics …
computational science and engineering. The objective is to integrate data and physics …
A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes
Quantifying biomechanical properties of the human vasculature could deepen our
understanding of cardiovascular diseases. Standard nonlinear regression in constitutive …
understanding of cardiovascular diseases. Standard nonlinear regression in constitutive …
Mycrunchgpt: A llm assisted framework for scientific machine learning
Scientific machine learning (SciML) has advanced recently across many different areas in
computational science and engineering. The objective is to integrate data and physics …
computational science and engineering. The objective is to integrate data and physics …
Learning in latent spaces improves the predictive accuracy of deep neural operators
Operator regression provides a powerful means of constructing discretization-invariant
emulators for partial-differential equations (PDEs) describing physical systems. Neural …
emulators for partial-differential equations (PDEs) describing physical systems. Neural …
Mitigating spectral bias for the multiscale operator learning
Neural operators have emerged as a powerful tool for learning the mapping between infinite-
dimensional parameter and solution spaces of partial differential equations (PDEs). In this …
dimensional parameter and solution spaces of partial differential equations (PDEs). In this …