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Model Discovery

discovering PDEs from spatiotemporal data

Papers

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Showing 26–50 of 87 papers

TitleStatusHype
Automated Statistical Model Discovery with Language Models—0
Operator Learning: Algorithms and Analysis—0
Process mining for self-regulated learning assessment in e-learning—0
Optimal Pricing for Data-Augmented AutoML Marketplaces—0
Exploring hyperelastic material model discovery for human brain cortex: multivariate analysis vs. artificial neural network approaches—0
On sparse regression, Lp-regularization, and automated model discovery—0
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations—0
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics—0
Naming Practices of Pre-Trained Models in Hugging Face—0
End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning—0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODECode1
A Competitive Learning Approach for Specialized Models: A Solution for Complex Physical Systems with Distinct Functional Regimes—0
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence—0
Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approachCode0
Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial OptimizationCode0
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery—0
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning—0
Interpretable Scientific Discovery with Symbolic Regression: A ReviewCode0
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Shape-Aware Masking for Inpainting in Medical Imaging—0
Learning Sparse Nonlinear Dynamics via Mixed-Integer OptimizationCode1
Error-in-variables modelling for operator learning—0
Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effectsCode0
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders—0
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