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Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …
combine data with mathematical laws in physics and engineering in a profound way …
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear map**s between infinite-dimensional function
spaces via deep neural networks. As promising surrogate solvers of partial differential …
spaces via deep neural networks. As promising surrogate solvers of partial differential …
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Deep neural operators can learn operators map** between infinite-dimensional function
spaces via deep neural networks and have become an emerging paradigm of scientific …
spaces via deep neural networks and have become an emerging paradigm of scientific …
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
Uncertainty quantification (UQ) in machine learning is currently drawing increasing research
interest, driven by the rapid deployment of deep neural networks across different fields, such …
interest, driven by the rapid deployment of deep neural networks across different fields, such …
Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators
Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly
critical as neural networks (NNs) are being widely adopted in addressing complex problems …
critical as neural networks (NNs) are being widely adopted in addressing complex problems …
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
Modern climate projections lack adequate spatial and temporal resolution due to
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
RiemannONets: Interpretable neural operators for Riemann problems
Develo** the proper representations for simulating high-speed flows with strong shock
waves, rarefactions, and contact discontinuities has been a long-standing question in …
waves, rarefactions, and contact discontinuities has been a long-standing question in …
The impact of large language models on scientific discovery: a preliminary study using gpt-4
MR AI4Science, MA Quantum - arxiv preprint arxiv:2311.07361, 2023 - arxiv.org
In recent years, groundbreaking advancements in natural language processing have
culminated in the emergence of powerful large language models (LLMs), which have …
culminated in the emergence of powerful large language models (LLMs), which have …
VB-DeepONet: A Bayesian operator learning framework for uncertainty quantification
Neural network based data-driven operator learning schemes have shown tremendous
potential in computational mechanics. DeepONet is one such neural network architecture …
potential in computational mechanics. DeepONet is one such neural network architecture …