kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
cs.LG
Submitted: 2025-12-24
Updated: 2026-01-08
DOI: 10.21105/joss.10342
Code: https://github.com/Machine-Learning-Dynamical-Systems/kooplearn
Project page: https://kooplearn.readthedocs.io
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Koopman-Equivariant Gaussian Processes
- Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks