Distilling free-form natural laws from experimental data

M Schmidt, H Lipson - science, 2009 - science.org
For centuries, scientists have attempted to identify and document analytical laws that
underlie physical phenomena in nature. Despite the prevalence of computing power, the …

Automated discovery of fundamental variables hidden in experimental data

B Chen, K Huang, S Raghupathi… - Nature Computational …, 2022 - nature.com
All physical laws are described as mathematical relationships between state variables.
These variables give a complete and non-redundant description of the relevant system …

[HTML][HTML] Could a neuroscientist understand a microprocessor?

E Jonas, KP Kording - PLoS computational biology, 2017 - journals.plos.org
There is a popular belief in neuroscience that we are primarily data limited, and that
producing large, multimodal, and complex datasets will, with the help of advanced data …

Discovering causal relations and equations from data

G Camps-Valls, A Gerhardus, U Ninad, G Varando… - Physics Reports, 2023 - Elsevier
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Beyond the data deluge

G Bell, T Hey, A Szalay - Science, 2009 - science.org
Since at least Newton's laws of motion in the 17th century, scientists have recognized
experimental and theoretical science as the basic research paradigms for understanding …

Symbolic pregression: Discovering physical laws from distorted video

SM Udrescu, M Tegmark - Physical Review E, 2021 - APS
We present a method for unsupervised learning of equations of motion for objects in raw and
optionally distorted unlabeled synthetic video (or, more generally, for discovering and …

[图书][B] Understanding nonlinear dynamics

D Kaplan, L Glass - 2012 - books.google.com
Mathematics is playing an ever more important role in the physical and biological sciences,
provoking a blurring of boundaries between scientific disciplines and a resurgence of …

Multistep neural networks for data-driven discovery of nonlinear dynamical systems

M Raissi, P Perdikaris, GE Karniadakis - arXiv preprint arXiv:1801.01236, 2018 - arxiv.org
The process of transforming observed data into predictive mathematical models of the
physical world has always been paramount in science and engineering. Although data is …

[HTML][HTML] Discerning non-autonomous dynamics

PT Clemson, A Stefanovska - Physics Reports, 2014 - Elsevier
Abstract Structure and function go hand in hand. However, while a complex structure can be
relatively safely broken down into the minutest parts, and technology is now delving into …

Data-driven discovery of coordinates and governing equations

K Champion, B Lusch, JN Kutz… - Proceedings of the …, 2019 - National Acad Sciences
The discovery of governing equations from scientific data has the potential to transform data-
rich fields that lack well-characterized quantitative descriptions. Advances in sparse …