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Model Discovery

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/PaulsonLab/SyMANTIC

공식 코드

SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond

https://github.com/PhIMaL/DeePyMoD

공식 코드

DeepMoD: Deep learning for Model Discovery in noisy data

https://github.com/PredictiveIntelligenceLab/BayesianDifferentiableProgramming

공식 코드

Bayesian differential programming for robust systems identification under uncertainty

https://github.com/PredictiveIntelligenceLab/GP-NODEs

공식 코드

Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data

https://github.com/SCAI-JHU/AutoToM

공식 코드

AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind

https://github.com/biomathlab/PDE-Learning-few-time-samples

공식 코드

Learning Equations from Biological Data with Limited Time Samples

https://github.com/charlesdelahunt/sindytoolkitforhighnoise

공식 코드

A toolkit for data-driven discovery of governing equations in high-noise regimes

https://github.com/dcn-fau-avh/edmd-sos

공식 코드

Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization

https://github.com/dynamicslab/NormalFormAE

공식 코드

Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations

https://github.com/dynamicslab/modified-SINDy

공식 코드

Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data

https://github.com/dynamicslab/pysindy

공식 코드

PySINDy: A comprehensive Python package for robust sparse system identification

https://github.com/georgestod/multi_deepmod

공식 코드

Discovering PDEs from Multiple Experiments

https://github.com/georgestod/sparsistent_model_disco

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Model discovery in the sparse sampling regime

https://github.com/jameslu01/tdnode

공식 코드

Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE

https://github.com/johnnardini/me-eql

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Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)

https://github.com/kanishkg/boxing-gym

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BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery

https://github.com/livingmatterlab/cann

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A new family of Constitutive Artificial Neural Networks towards automated model discovery

https://github.com/meganebers/discrepancy-modeling-framework-code

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Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects

https://github.com/mgolden30/fastsparseregression

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Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight

https://github.com/mgolden30/spider

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Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight

https://github.com/mjayadharan/DAE-FINDER_dev

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SODAs: Sparse Optimization for the Discovery of Differential and Algebraic Equations

https://github.com/paulsonlab/torchsisso

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TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery

https://github.com/pongpisit-thanasutives/ubic

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Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery

https://github.com/sensorlab/autopathloss

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Automated Modeling Method for Pathloss Model Discovery

https://github.com/siddharth-prabhu/bilevelparameterestimation

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Bi-Level optimization for parameter estimation of differential equations using interpolation

https://github.com/tirtho109/vanillapinnsvsfbpinns

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Towards Model Discovery Using Domain Decomposition and PINNs

https://github.com/viniviena/ude_chromatography

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Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach

https://github.com/wesg52/sindy_mio_paper

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Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization

https://github.com/zhaochenneu/EQDiscovery-1

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Physics-informed learning of governing equations from scarce data

https://github.com/zhoufangqin/hytas

공식 코드

HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis

https://github.com/CP3-Origins/Things-to-bench

Interpretable Scientific Discovery with Symbolic Regression: A Review

https://github.com/MoSubaiei/modified-SINDy

Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data

https://github.com/PhIMaL/DeePyMoD_torch

DeepMoD: Deep learning for Model Discovery in noisy data

https://github.com/chuanyangjin/MMToM-QA

AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind

https://github.com/jbramburger/data-clf

Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization

https://github.com/negrinho/deep_architect_legacy

DeepArchitect: Automatically Designing and Training Deep Architectures

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.