Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Fueled by breakthrough technology developments, the biological, biomedical, and
behavioral sciences are now collecting more data than ever before. There is a critical need …
behavioral sciences are now collecting more data than ever before. There is a critical need …
Multiscale modeling meets machine learning: What can we learn?
Abstract Machine learning is increasingly recognized as a promising technology in the
biological, biomedical, and behavioral sciences. There can be no argument that this …
biological, biomedical, and behavioral sciences. There can be no argument that this …
[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 …
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 …
On neural differential equations
P Kidger - arxiv preprint arxiv:2202.02435, 2022 - arxiv.org
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
Universal differential equations for scientific machine learning
In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …
Hidden physics models: Machine learning of nonlinear partial differential equations
M Raissi, GE Karniadakis - Journal of Computational Physics, 2018 - Elsevier
While there is currently a lot of enthusiasm about “big data”, useful data is usually “small”
and expensive to acquire. In this paper, we present a new paradigm of learning partial …
and expensive to acquire. In this paper, we present a new paradigm of learning partial …
Data-driven discovery of partial differential equations
We propose a sparse regression method capable of discovering the governing partial
differential equation (s) of a given system by time series measurements in the spatial …
differential equation (s) of a given system by time series measurements in the spatial …
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
Sparse model identification enables the discovery of nonlinear dynamical systems purely
from data; however, this approach is sensitive to noise, especially in the low-data limit. In this …
from data; however, this approach is sensitive to noise, especially in the low-data limit. In this …
[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 …