Model Discovery

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Most implemented papers archive 2025-07-28

30 shown of 33 papers with code (87 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind
  21 Feb 2025 2 repositories listed Syntology ran 3 of 20 samples · 17 unverified
  Based on the uncertainty of the inference, it iteratively refines the model, by introducing additional mental variables and/or incorporating more timesteps in the context.
• Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight
  14 May 2024 2 repositories listed
  There exist endless examples of dynamical systems with vast available data and unsatisfying mathematical descriptions.
• Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization
  2 Mar 2023 2 repositories listed
  We present a flexible data-driven method for dynamical system analysis that does not require explicit model discovery.
• A new family of Constitutive Artificial Neural Networks towards automated model discovery
  15 Sep 2022 2 repositories listed
  For more than 100 years, chemical, physical, and material scientists have proposed competing constitutive models to best characterize the behavior of natural and man-made materials in response to mechanical loading.
• Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data
  12 Sep 2020 2 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the discovery of parsimonious dynamic models and governing equations from time-series data.
• DeepMoD: Deep learning for Model Discovery in noisy data
  20 Apr 2019 2 repositories listed Syntology ran 0 of 18 samples · 18 unverified
  We introduce DeepMoD, a Deep learning based Model Discovery algorithm.
• Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)
  10 Jun 2025 1 repository listed
  Agent-based modeling (ABM) is a powerful tool for understanding self-organizing biological systems, but it is computationally intensive and often not analytically tractable.
• Bi-Level optimization for parameter estimation of differential equations using interpolation
  31 May 2025 1 repository listed
  In this paper, we use a bi-level optimization framework that leverages interpolation and exploits the structure of the differential equation to solve an inner convex optimization problem.
• Automated Modeling Method for Pathloss Model Discovery
  29 May 2025 1 repository listed
  Inspired by recent advancements in AI, this paper proposes a novel approach that accelerates the discovery of path loss models while maintaining interpretability.
• SODAs: Sparse Optimization for the Discovery of Differential and Algebraic Equations
  8 Mar 2025 1 repository listed
  Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by…
• SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond
  5 Feb 2025 1 repository listed
  Symbolic regression (SR) is an emerging branch of machine learning focused on discovering simple and interpretable mathematical expressions from data.
• BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery
  2 Jan 2025 1 repository listed
  To quantitatively evaluate a scientific agent's ability to collect informative experimental data, we compute the expected information gain (EIG), an information-theoretic quantity which measures how much an experiment…
• TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery
  2 Oct 2024 1 repository listed
  Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact representations of complex data.
• Towards Model Discovery Using Domain Decomposition and PINNs
  2 Oct 2024 1 repository listed
  We enhance machine learning algorithms for learning model parameters in complex systems represented by ordinary differential equations (ODEs) with domain decomposition methods.
• HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis
  23 Jul 2024 1 repository listed
  Hyperspectral Imaging (HSI) plays an increasingly critical role in precise vision tasks within remote sensing, capturing a wide spectrum of visual data.
• Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery
  20 Aug 2023 1 repository listed
  We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently governs noisy spatial-temporal…
• Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE
  2 Aug 2023 1 repository listed
  We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data.
• Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach
  22 Mar 2023 1 repository listed
  The study successfully reconstructed sorption uptake kinetics using sparse and symbolic regression, and accurately predicted breakthrough curves using identified polynomials, highlighting the potential of the proposed…
• Interpretable Scientific Discovery with Symbolic Regression: A Review
  20 Nov 2022 1 repository listed
  Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data.
• Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization
  1 Jun 2022 1 repository listed Syntology ran 0 of 10 samples · 10 unverified
  Discovering governing equations of complex dynamical systems directly from data is a central problem in scientific machine learning.
• Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects
  10 Mar 2022 1 repository listed
  We introduce a discrepancy modeling framework to identify the missing physics and resolve the model-measurement mismatch with two distinct approaches: (i) by learning a model for the evolution of systematic state-space…
• PySINDy: A comprehensive Python package for robust sparse system identification
  12 Nov 2021 1 repository listed
  Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community.
• A toolkit for data-driven discovery of governing equations in high-noise regimes
  8 Nov 2021 1 repository listed
  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.
• Discovering PDEs from Multiple Experiments
  24 Sep 2021 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations.
• Sparsistent Model Discovery
  22 Jun 2021 1 repository listed
  Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields.
• Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations
  9 Jun 2021 1 repository listed
  In this work, we introduce deep learning autoencoders to discover coordinate transformations that capture the underlying parametric dependence of a dynamical system in terms of its canonical normal form, allowing for a…
• Model discovery in the sparse sampling regime
  2 May 2021 1 repository listed
  To improve the physical understanding and the predictions of complex dynamic systems, such as ocean dynamics and weather predictions, it is of paramount interest to identify interpretable models from coarsely and…
• Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data
  4 Mar 2021 1 repository listed
  This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems.
• Sparsely constrained neural networks for model discovery of PDEs
  9 Nov 2020 1 repository listed
  Sparse regression on a library of candidate features has developed as the prime method to discover the partial differential equation underlying a spatio-temporal data-set.
• Learning Equations from Biological Data with Limited Time Samples
  19 May 2020 1 repository listed
  Equation learning methods present a promising tool to aid scientists in the modeling process for biological data.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.