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 …
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 …
[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 …
Physics‐informed deep neural networks for learning parameters and constitutive relationships in subsurface flow problems
AM Tartakovsky, CO Marrero… - Water Resources …, 2020 - Wiley Online Library
We present a physics‐informed deep neural network (DNN) method for estimating hydraulic
conductivity in saturated and unsaturated flows governed by Darcy's law. For saturated flow …
conductivity in saturated and unsaturated flows governed by Darcy's law. For saturated flow …
[图书][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods
Owing to recent advancements in sensor technology, data mining, Machine Learning (ML)
and cloud computation, Structural Health Monitoring (SHM) based on a data-driven …
and cloud computation, Structural Health Monitoring (SHM) based on a data-driven …
Physics-informed deep learning for traffic state estimation: A survey and the outlook
For its robust predictive power (compared to pure physics-based models) and sample-
efficient training (compared to pure deep learning models), physics-informed deep learning …
efficient training (compared to pure deep learning models), physics-informed deep learning …
Pi-vae: Physics-informed variational auto-encoder for stochastic differential equations
We propose a new class of physics-informed neural networks, called the Physics-Informed
Variational Auto-Encoder (PI-VAE), to solve stochastic differential equations (SDEs) or …
Variational Auto-Encoder (PI-VAE), to solve stochastic differential equations (SDEs) or …
Smart-PGSim: Using neural network to accelerate AC-OPF power grid simulation
In this work we address the problem of accelerating complex power-grid simulation through
machine learning (ML). Specifically, we develop a framework, Smart-PGSim, which …
machine learning (ML). Specifically, we develop a framework, Smart-PGSim, which …
Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database
We study the applicability of tools developed by the computer vision community for feature
learning and semantic image inpainting to perform data reconstruction of fluid turbulence …
learning and semantic image inpainting to perform data reconstruction of fluid turbulence …