Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems
Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …
training data. A potential solution is the additional integration of prior knowledge into the …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Toward causal representation learning
The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
Learning mesh-based simulation with graph networks
Mesh-based simulations are central to modeling complex physical systems in many
disciplines across science and engineering. Mesh representations support powerful …
disciplines across science and engineering. Mesh representations support powerful …
Learning to simulate complex physics with graph networks
A Sanchez-Gonzalez, J Godwin… - International …, 2020 - proceedings.mlr.press
Here we present a machine learning framework and model implementation that can learn to
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Explainable machine learning for scientific insights and discoveries
Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …
areas in the extraction of essential information from data. An exciting and relatively recent …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
A physics-informed diffusion model for high-fidelity flow field reconstruction
Abstract Machine learning models are gaining increasing popularity in the domain of fluid
dynamics for their potential to accelerate the production of high-fidelity computational fluid …
dynamics for their potential to accelerate the production of high-fidelity computational fluid …
Interaction networks for learning about objects, relations and physics
Abstract Reasoning about objects, relations, and physics is central to human intelligence,
and a key goal of artificial intelligence. Here we introduce the interaction network, a model …
and a key goal of artificial intelligence. Here we introduce the interaction network, a model …