Scientific discovery in the age of artificial intelligence
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …
and accelerate research, helping scientists to generate hypotheses, design experiments …
Statistical inference links data and theory in network science
The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …
increasing. Surprisingly, the development of theory and domain-specific applications often …
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 …
Interpretable machine learning for science with PySR and SymbolicRegression. jl
M Cranmer - arXiv preprint arXiv:2305.01582, 2023 - arxiv.org
PySR is an open-source library for practical symbolic regression, a type of machine learning
which aims to discover human-interpretable symbolic models. PySR was developed to …
which aims to discover human-interpretable symbolic models. PySR was developed to …
End-to-end symbolic regression with transformers
PA Kamienny, S d'Ascoli, G Lample… - Advances in Neural …, 2022 - proceedings.neurips.cc
Symbolic regression, the task of predicting the mathematical expression of a function from
the observation of its values, is a difficult task which usually involves a two-step procedure …
the observation of its values, is a difficult task which usually involves a two-step procedure …
Discovering symbolic models from deep learning with inductive biases
M Cranmer, A Sanchez Gonzalez… - Advances in neural …, 2020 - proceedings.neurips.cc
We develop a general approach to distill symbolic representations of a learned deep model
by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The …
by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The …
Discovering causal relations and equations from data
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 …
questions about why natural phenomena occur and to make testable models that explain the …
Data, measurement and empirical methods in the science of science
The advent of large-scale datasets that trace the workings of science has encouraged
researchers from many different disciplinary backgrounds to turn scientific methods into …
researchers from many different disciplinary backgrounds to turn scientific methods into …
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
W Tenachi, R Ibata, FI Diakogiannis - The Astrophysical Journal, 2023 - iopscience.iop.org
Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …
expressions that fit data. While recent advances in deep learning have generated renewed …
Rediscovering orbital mechanics with machine learning
We present an approach for using machine learning to automatically discover the governing
equations and unknown properties (in this case, masses) of real physical systems from …
equations and unknown properties (in this case, masses) of real physical systems from …