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Model Discovery
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discovering PDEs from spatiotemporal data
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)
Bi-Level optimization for parameter estimation of differential equations using interpolation
Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems
Automated Modeling Method for Pathloss Model Discovery
Model Discovery with Grammatical Evolution. An Experiment with Prime Numbers
Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
Optimizing Hard Thresholding for Sparse Model Discovery
Discovering dynamical laws for speech gestures
Coordinated Ramp Metering Control based on Scalable Nonlinear Traffic Dynamics Model Discovery in a Large Network
SODAs: Sparse Optimization for the Discovery of Differential and Algebraic Equations
AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond
A Cutting Mechanics-based Machine Learning Modeling Method to Discover Governing Equations of Machining Dynamics
NOMTO: Neural Operator-based symbolic Model approximaTion and discOvery
BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
AutoTurb: Using Large Language Models for Automatic Algebraic Model Discovery of Turbulence Closure
Towards Model Discovery Using Domain Decomposition and PINNs
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery
Data-driven model discovery with Kolmogorov-Arnold networks
HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis
GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations
Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight
Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks
Automated Statistical Model Discovery with Language Models
Operator Learning: Algorithms and Analysis
Process mining for self-regulated learning assessment in e-learning
Optimal Pricing for Data-Augmented AutoML Marketplaces
Exploring hyperelastic material model discovery for human brain cortex: multivariate analysis vs. artificial neural network approaches
On sparse regression, Lp-regularization, and automated model discovery
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
Naming Practices of Pre-Trained Models in Hugging Face
End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.