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Model Discovery

discovering PDEs from spatiotemporal data

Papers

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Showing 51–75 of 87 papers

TitleStatusHype
Quantum Model-Discovery—0
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)—0
Shape-Aware Masking for Inpainting in Medical Imaging—0
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence—0
Discovery of Governing Equations with Recursive Deep Neural Networks—0
End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning—0
Error-in-variables modelling for operator learning—0
Exploiting Leaderboards for Large-Scale Distribution of Malicious Models—0
Exploring hyperelastic material model discovery for human brain cortex: multivariate analysis vs. artificial neural network approaches—0
Naming Practices of Pre-Trained Models in Hugging Face—0
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics—0
FactorBase: SQL for Learning A Multi-Relational Graphical Model—0
Feature Recommendation for Structural Equation Model Discovery in Process Mining—0
Fully differentiable model discovery—0
GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations—0
GPTIPS 2: an open-source software platform for symbolic data mining—0
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations—0
Discovering PDEs from Multiple ExperimentsCode0
Automated Modeling Method for Pathloss Model DiscoveryCode0
Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effectsCode0
Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approachCode0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Learning Equations from Biological Data with Limited Time SamplesCode0
DeepArchitect: Automatically Designing and Training Deep ArchitecturesCode0
Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equationsCode0
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