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Model Discovery

discovering PDEs from spatiotemporal data

Papers

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Showing 51–75 of 87 papers

TitleStatusHype
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)—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
Quantum Model-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
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
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