Machine learning for molecular and materials science
Here we summarize recent progress in machine learning for the chemical sciences. We
outline machine-learning techniques that are suitable for addressing research questions in …
outline machine-learning techniques that are suitable for addressing research questions in …
Deep learning for the design of photonic structures
Innovative approaches and tools play an important role in sha** design, characterization
and optimization for the field of photonics. As a subset of machine learning that learns …
and optimization for the field of photonics. As a subset of machine learning that learns …
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
It is widely known that neural networks (NNs) are universal approximators of continuous
functions. However, a less known but powerful result is that a NN with a single hidden layer …
functions. However, a less known but powerful result is that a NN with a single hidden layer …
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
We introduce physics-informed neural networks–neural networks that are trained to solve
supervised learning tasks while respecting any given laws of physics described by general …
supervised learning tasks while respecting any given laws of physics described by general …
Interpretable machine learning: Fundamental principles and 10 grand challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
[BOOK][B] Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz - 2022 - books.google.com
Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
M Raissi, A Yazdani, GE Karniadakis - Science, 2020 - science.org
For centuries, flow visualization has been the art of making fluid motion visible in physical
and biological systems. Although such flow patterns can be, in principle, described by the …
and biological systems. Although such flow patterns can be, in principle, described by the …
Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations
We introduce physics informed neural networks--neural networks that are trained to solve
supervised learning tasks while respecting any given law of physics described by general …
supervised learning tasks while respecting any given law of physics described by general …
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 …