Cover Image

Sparse Identification of Nonlinear Dynamics with Conformal Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Fasel, Urban
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computational Engineering, Finance, and Science Dynamical Systems
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SINDyG: Sparse Identification of Nonlinear Dynamical Systems from Graph-Structured Data, with Applications to Stuart-Landau Oscillator Networks
by: Basiri, Mohammad Amin, et al.
Published: (2024)

VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
by: Conti, Paolo, et al.
Published: (2024)

PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
by: Ye, David, et al.
Published: (2025)

Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics -- Rotating Detonation Engines
by: Bao, Yuxuan, et al.
Published: (2026)

SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
by: Miñoza, Jose Marie Antonio
Published: (2026)

Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems
by: Wolf, Florian, et al.
Published: (2024)

Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis
by: Agarwal, Rahul, et al.
Published: (2025)

Data-Assisted Non-Intrusive Model Reduction for Forced Nonlinear Finite Elements Models
by: Cenedese, Mattia, et al.
Published: (2023)

FMint-SDE: A Multimodal Foundation Model for Accelerating Numerical Simulation of SDEs via Error Correction
by: Yuan, Jiaxin, et al.
Published: (2025)

Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning
by: Faroughi, Salah A, et al.
Published: (2026)

Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems
by: Kapadia, Harshit, et al.
Published: (2025)

Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace
by: Pepper, Nick, et al.
Published: (2026)

DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction
by: Elrefaie, Mohamed, et al.
Published: (2024)

WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics
by: Ashton, Neil, et al.
Published: (2024)

DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
by: Ashton, Neil, et al.
Published: (2024)

AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics
by: Ashton, Neil, et al.
Published: (2024)

NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design
by: Yagoubi, Mouadh, et al.
Published: (2024)

Exact nonlinear state estimation
by: Chipilski, Hristo G.
Published: (2023)

FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model
by: Song, Zezheng, et al.
Published: (2024)

A Stochastic Dynamic Network Model of the Space Environment
by: Wang, Yirui, et al.
Published: (2024)

Prediction of Steady-State Flow through Porous Media Using Machine Learning Models
by: Wang, Jinhong, et al.
Published: (2026)

Reduced order modelling of Hopf bifurcations for the Navier-Stokes equations through invariant manifolds
by: Colombo, Alessio, et al.
Published: (2025)

Circularity of Thermodynamical Material Networks: Indicators, Examples, and Algorithms
by: Zocco, Federico
Published: (2022)

Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems
by: Sharma, Harsh, et al.
Published: (2024)

Data-free Non-intrusive Model Reduction for Nonlinear Finite Element Models via Spectral Submanifolds
by: Li, Mingwu, et al.
Published: (2024)

Learning Fluid-Structure Interaction with Physics-Informed Machine Learning and Immersed Boundary Methods
by: Farea, Afrah, et al.
Published: (2025)

A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks
by: Zhang, Ziyang, et al.
Published: (2025)

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming
by: Liu, Xiaofeng, et al.
Published: (2025)

Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities
by: Chen, Cheng, et al.
Published: (2025)

Multi-level datasets training method in Physics-Informed Neural Networks
by: Tsai, Yao-Hsuan, et al.
Published: (2025)

Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows
by: Cutforth, Murray, et al.
Published: (2025)

JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows
by: Bezgin, Deniz A., et al.
Published: (2024)

On instabilities in neural network-based physics simulators
by: Floryan, Daniel
Published: (2024)

Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
by: Yang, Sunwoong, et al.
Published: (2024)

Model-based deep reinforcement learning for accelerated learning from flow simulations
by: Weiner, Andre, et al.
Published: (2024)

C(NN)FD -- a deep learning framework for turbomachinery CFD analysis
by: Bruni, Giuseppe, et al.
Published: (2023)

Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics
by: Bruni, Giuseppe, et al.
Published: (2023)

A TVD neural network closure and application to turbulent combustion
by: Suh, Seung Won, et al.
Published: (2024)

A MgNO Method for Multiphase Flow in Porous Media
by: Liu, Xinliang, et al.
Published: (2024)

Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins
by: Yanik, Yasar, et al.
Published: (2026)