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 …
[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 …
A review of designs and applications of echo state networks
Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …
PPINN: Parareal physics-informed neural network for time-dependent PDEs
Physics-informed neural networks (PINNs) encode physical conservation laws and prior
physical knowledge into the neural networks, ensuring the correct physics is represented …
physical knowledge into the neural networks, ensuring the correct physics is represented …
Physics guided neural network for machining tool wear prediction
Tool wear prediction is of significance to improve the safety and reliability of machining tools,
given their widespread applications in nearly every branch of manufacturing. Mathematical …
given their widespread applications in nearly every branch of manufacturing. Mathematical …
Robustness of LSTM neural networks for multi-step forecasting of chaotic time series
M Sangiorgio, F Dercole - Chaos, Solitons & Fractals, 2020 - Elsevier
Recurrent neurons (and in particular LSTM cells) demonstrated to be efficient when used as
basic blocks to build sequence to sequence architectures, which represent the state-of-the …
basic blocks to build sequence to sequence architectures, which represent the state-of-the …
Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study
In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …
traditional time-series forecasting approaches in many contexts, including univariate and …
DPM: A novel training method for physics-informed neural networks in extrapolation
We present a method for learning dynamics of complex physical processes described by
time-dependent nonlinear partial differential equations (PDEs). Our particular interest lies in …
time-dependent nonlinear partial differential equations (PDEs). Our particular interest lies in …
Robust optimization and validation of echo state networks for learning chaotic dynamics
An approach to the time-accurate prediction of chaotic solutions is by learning temporal
patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing …
patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing …
Machine learning and physics: A survey of integrated models
A Seyyedi, M Bohlouli, SN Oskoee - ACM Computing Surveys, 2023 - dl.acm.org
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 …