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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 …
Machine learning and physics: A survey of integrated models
Predictive modeling of various systems around the world is extremely essential from the
physics and engineering perspectives. The recognition of different systems and the capacity …
physics and engineering perspectives. The recognition of different systems and the capacity …
[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 …
Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
We are interested in the development of surrogate models for uncertainty quantification and
propagation in problems governed by stochastic PDEs using a deep convolutional encoder …
propagation in problems governed by stochastic PDEs using a deep convolutional encoder …
Adversarial uncertainty quantification in physics-informed neural networks
We present a deep learning framework for quantifying and propagating uncertainty in
systems governed by non-linear differential equations using physics-informed neural …
systems governed by non-linear differential equations using physics-informed neural …
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
State-of-the-art computer codes for simulating real physical systems are often characterized
by vast number of input parameters. Performing uncertainty quantification (UQ) tasks with …
by vast number of input parameters. Performing uncertainty quantification (UQ) tasks with …
Transfer learning based multi-fidelity physics informed deep neural network
For many systems in science and engineering, the governing differential equation is either
not known or known in an approximate sense. Analyses and design of such systems are …
not known or known in an approximate sense. Analyses and design of such systems are …
Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods
Monitoring the health of lithium-ion batteries' internal components as they age is crucial for
optimizing cell design and usage control strategies. However, quantifying component-level …
optimizing cell design and usage control strategies. However, quantifying component-level …
A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic–vibration interaction problems
We propose an efficient Monte Carlo simulation method to address the multivariate
uncertainties in acoustic–vibration interaction systems. The deep neural network acts as a …
uncertainties in acoustic–vibration interaction systems. The deep neural network acts as a …
Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks
Stochastic partial differential equations (SPDEs) are ubiquitous in engineering and
computational sciences. The stochasticity arises as a consequence of uncertainty in input …
computational sciences. The stochasticity arises as a consequence of uncertainty in input …