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Model Discovery

discovering PDEs from spatiotemporal data

Papers

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Showing 1–50 of 87 papers

TitleStatusHype
AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of MindCode2
PySINDy: A comprehensive Python package for robust sparse system identificationCode2
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model DiscoveryCode1
Sparsely constrained neural networks for model discovery of PDEsCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODECode1
Physics-informed learning of governing equations from scarce dataCode1
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and BeyondCode1
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
Learning Sparse Nonlinear Dynamics via Mixed-Integer OptimizationCode1
Bayesian differential programming for robust systems identification under uncertaintyCode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Causal KL: Evaluating Causal Discovery—0
Computational model discovery with reinforcement learning—0
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery—0
Coordinated Ramp Metering Control based on Scalable Nonlinear Traffic Dynamics Model Discovery in a Large Network—0
Data-driven model discovery with Kolmogorov-Arnold networks—0
Discovering dynamical laws for speech gestures—0
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders—0
Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks—0
NOMTO: Neural Operator-based symbolic Model approximaTion and discOvery—0
A Competitive Learning Approach for Specialized Models: A Solution for Complex Physical Systems with Distinct Functional Regimes—0
A Cutting Mechanics-based Machine Learning Modeling Method to Discover Governing Equations of Machining Dynamics—0
Optimal Pricing for Data-Augmented AutoML Marketplaces—0
A Nonparametric Bayesian Approach to Acoustic Model Discovery—0
Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models generated by Mining Event-Log Data in Issue Tracking System—0
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling—0
Automated Statistical Model Discovery with Language Models—0
Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency—0
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning—0
AutoTurb: Using Large Language Models for Automatic Algebraic Model Discovery of Turbulence Closure—0
Causal Inductive Synthesis Corpus—0
Learning Cognitive Models using Neural Networks—0
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems—0
Learning Sparse Causal Models is not NP-hard—0
Mining Local Process Models—0
Model Discovery with Grammatical Evolution. An Experiment with Prime Numbers—0
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks—0
Neuro-Reachability of Networked Microgrids—0
Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems—0
On sparse regression, Lp-regularization, and automated model discovery—0
Operator Learning: Algorithms and Analysis—0
Optimizing Hard Thresholding for Sparse Model Discovery—0
OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs—0
Pre and Post Counting for Scalable Statistical-Relational Model Discovery—0
Precision and Fitness in Object-Centric Process Mining—0
Process mining for self-regulated learning assessment in e-learning—0
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)—0
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