Equation Discovery
26 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Automated Mathematical Equation Structure Discovery for Visual Analysis
carolinepacheco/equation_discovery_scene_analysis
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In this paper, we focus on recent AI advances to present a novel framework for automatically discovering equations from scratch with little human intervention to deal with the different challenges encountered in real-world scenarios.
Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case
Models of many engineering and natural systems are imperfect.
PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning
We introduce a new approach for PDE discovery that uses two Rational Neural Networks and a principled sparse regression algorithm to identify the hidden dynamics that govern a system's response.
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
envfluids/py2d
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These closures depend on nonlinear combinations of gradients of filtered variables, with constants that are independent of the fluid/flow properties and only depend on filter type/size.
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
deep-symbolic-mathematics/Multimodal-Math-Pretraining
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To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings.
Learning and Interpreting Potentials for Classical Hamiltonian Systems
We demonstrate this approach for several systems, including oscillators, a central force problem, and a problem of two charged particles in a classical Coulomb potential.
Probabilistic Grammars for Equation Discovery
brencej/ProGED
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Equation discovery, also known as symbolic regression, is a type of automated modeling that discovers scientific laws, expressed in the form of equations, from observed data and expert knowledge.
AI Descartes: Combining Data and Theory for Derivable Scientific Discovery
We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression.
A toolkit for data-driven discovery of governing equations in high-noise regimes
Second, we propose a technique, applicable to any model discovery method based on x' = f(x), to assess the accuracy of a discovered model in the context of non-unique solutions due to noisy data.
Learning Anisotropic Interaction Rules from Individual Trajectories in a Heterogeneous Cellular Population
Motivated by the success of IPS models to describe the spatial movement of organisms, we develop WSINDy for second order IPSs to model the movement of communities of cells.
