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Equation Discovery
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience
Governing Equation Discovery from Data Based on Differential Invariants
Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
On the definition and importance of interpretability in scientific machine learning
LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models
Grammar-based Ordinary Differential Equation Discovery
Graph-Eq: Discovering Mathematical Equations using Graph Generative Models
Neural-Guided Equation Discovery
Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework
A Bayesian Approach for Discovering Time- Delayed Differential Equation from Data
Knowledge-aware equation discovery with automated background knowledge extraction
Equation discovery framework EPDE: Towards a better equation discovery
KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems
Governing equation discovery of a complex system from snapshots
Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
Discovering governing equation in structural dynamics from acceleration-only measurements
Discovering symbolic expressions with parallelized tree search
Amortized Equation Discovery in Hybrid Dynamical Systems
Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data
Symmetry-Informed Governing Equation Discovery
LLM4ED: Large Language Models for Automatic Equation Discovery
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
Statistical Mechanics of Dynamical System Identification
Mechanistic Neural Networks for Scientific Machine Learning
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots
Towards stable real-world equation discovery with assessing differentiating quality influence
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
Latent Space Symmetry Discovery
Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert
Towards true discovery of the differential equations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.