Nonlinear System Identification

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5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

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Latest papers 107

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Learning dynamical systems with biochemically informed neural ordinary differential equations
May 22, 2026Luis L. Fonseca, Reinhard C. Laubenbacher, Lucas BöttcherDynamical Systems Neural Processes
Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA · Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, Frankfurt am Main, 60322, Germany
Learning partially observed systems with neural Hamiltonian ordinary differential equations
May 22, 2026Sunniva Meltzer, Sølve Eidnes, Alexander Johannes StasikDynamical Systems Latent Dynamics Modeling
Department of Mathematics and Cybernetics, SINTEF Digital, Norway · Department of Physics, University of Oslo, Norway · Department of Data Science, Norwegian University of Life Science, Norway
Holomorphic Neural ODEs with Kolmogorov-Arnold Networks for Interpretable Discovery of Complex Dynamics
May 21, 2026Bhaskar Ranjan Karn, Dinesh KumarNonlinear System Identification Complex-Valued Neural Networks
Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot
May 18, 2026Harald Minde Hansen, Bjørn Kåre Sæbø, Kristin Y. Pettersen +2Robot Dynamics Identification Nonlinear System Identification
European Organization for Nuclear Research (CERN), Switzerland · Department of Engineering Cybernetics, Norwegian University of Science and Technology, NTNU, NO-7491 Trondheim, Norway
Geometric Dictionary Learning of Dynamical Systems with Optimal Transport
May 18, 2026Thibaut Germain, Sami Chemlal, Rémi Flamary +2Koopman Operator Learning Nonlinear System Identification
CMAP, Ecole Polytechnique · Istituto Italiano di Tecnologia & University of Novi Sad
Hypothesis-driven construction of mesoscopic dynamics
May 15, 2026Zhuoyuan Li, Aiqing Zhu, Qianxiao LiLatent Dynamics Modeling Nonlinear System Identification
Institute for Functional Intelligent Materials, National University of Singapore · Department of Mathematics, National University of Singapore
A Reproducible and Physically Feasible Dynamic Parameter Identification Framework for a Low-Cost Robot Arm
May 15, 2026Junji Oaki, Koki Yamane, Koki Inami +1Robot Dynamics Identification Parameter Identifiability
Institute of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Time-Varying Deep State Space Models for Sequences with Switching Dynamics
May 14, 2026Sanja Karilanova, Subhrakanti Dey, Ayça ÖzçelikkaleDynamical Systems Regime-Switching Dynamical Systems
Department of Electrical Engineering, Uppsala University, Sweden
A Novel Schur-Decomposition-Based Weight Projection Method for Stable State-Space Neural-Network Architectures
May 14, 2026Sergio Vanegas, Lasse Lensu, Fredy RuizNonlinear System Identification System Identification
Computational Engineering LUT University Lappeenranta, Finland · DEIB Politecnico di Milano Milan, Italy
Watch your neighbors: Training statistically accurate chaotic systems with local phase space information
May 14, 2026Joon-Hyuk Ko, Andrus Giraldo, Deok-Sun LeeSurrogate Modeling Nonlinear System Identification
Center for AI and Natural Sciences Korea Institute for Advanced Study Seoul, Korea 02455 · School of Computational Sciences Korea Institute for Advanced Study Seoul, Korea 02455
Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
May 12, 2026Maximino Linares, Guillaume Doras, Thomas HélieDynamical Systems Nonlinear System Identification
1IRCAM, 1, place Igor-Stravinsky, 75004, Paris, France · 2Sorbonne Université, 4, place Jussieu, 75005, Paris, France · 3CNRS, 3 rue Michel-Ange, 75016, Paris, France
FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression
May 12, 2026Zhiming Yu, Wangtao Lu, Xin LaiNonlinear System Identification Automated Feature Engineering
School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, China · Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction
May 12, 2026Florian Hess, Florian Götz, Daniel DurstewitzNonlinear System Identification Recurrent Neural Networks
1Dept. of Theoretical Neuroscience, Central Institute of Mental Health,2026 Mannheim, Germany · Faculty of Mathematics and Computer Science, Heidelberg University, Germany · Faculty of Physics and Astronomy, Heidelberg University, Germany +1
Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification
May 12, 2026Mehmet Ali Ferah, Tufan KumbasarUncertainty Quantification Nonlinear System Identification
Artificial Intelligence and Intelligent Systems Laboratory, Istanbul Technical University, Istanbul, Türkiye
Multi-Agent System Identification with Nonlinear Sheaf Diffusion
May 11, 2026Nivar Anwer, Hans Riess, Matthew HaleParameter Identifiability Nonlinear System Identification
Department of Computer Science, Georgia Tech · Department of Electrical & Computer Engineering, Georgia Tech
The finite expression method for turbulent dynamics with high-order moment recovery
May 11, 2026Xingjian Xu, Di Qi, Chunmei WangNonlinear System Identification Computational Fluid Dynamics
Department of Mathematics, University of Florida, Gainesvile, FL 32611 · Department of Mathematics, Purdue University, West Lafayette, IN 47907
Discovery of Nonlinear Dynamics with Automated Basis Function Generation
May 10, 2026Mohammad Amin Basiri, Charles NicholsonSparse Identification of Nonlinear Dynamics Nonlinear System Identification
Data Science and Analytics Institute, University of Oklahoma, Norman, OK, USA · School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA
Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials
May 9, 2026Vojtěch Votruba, Zequn He, Weilun Qiu +2Dynamical Systems Nonlinear System Identification
Mathematical Institute, Faculty of Mathematics and Physics, Charles University, Prague, 18675, Czech Republic · Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, 19104, PA, United States of America
A Behavioral Framework for Data-Driven Modeling of Nonlinear Systems in Vector-Valued Reproducing Kernel Hilbert Spaces
May 8, 2026Boya Hou, Maxim RaginskyDynamical Systems Nonlinear System Identification
Carl R. Woese Institute for Genomic Biology and the Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA · Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA
Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
May 7, 2026Umme Mahbuba Nabila, Paul Seurin, Linyu Lin +1Surrogate Modeling Sparse Identification of Nonlinear Dynamics
Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48109, United States · Nuclear Science & Technology Division, Idaho National Laboratory, Idaho Falls, ID 83415, United States · Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States
End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
May 7, 2026Carles Balsells-Rodas, Zhengrui Xiang, Xavier Sumba +1Dynamical Systems Latent Variable Models
Imperial College London
Koopman Identification of Nonlinear Systems via Reservoir Liftings
May 6, 2026Weibin Gu, Chen Yang, Lu ShiKoopman Operator Learning Nonlinear System Identification
Institute for AI Industry Research (AIR), Tsinghua University, Beijing, PR China · School of Engineering, China University of Petroleum-Beijing at Karamay, Xinjiang Uygur Autonomous Region, PR China
Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks
May 5, 2026Sreejata Dey, Guoxiang Grayson Tong, Jonathan F. MacArt +1Neural Surrogate Modeling Parameter Identifiability
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame · Department of Pediatrics, Stanford University · Department of Aerospace and Mechanical Engineering, University of Notre Dame
Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics
May 4, 2026Sani Biswas, Khursheed J. Ansari, Md. Nasim AkhtarNonlinear System Identification Physics-Informed ML
Centro de Modelamiento Matemático (CMM), Universidad de Chile & IRL 2807 - CNRS, Santiago, 8370456, Chile · Department of Mathematics, College of Science, King Khalid University, Abha, 61413, Saudi Arabia · Department of Mathematics, Presidency University, Kolkata, 700 073, India
Learning Koopman operators for coupled systems via information on governing equations of subsystems
May 3, 2026Tatsuya Naoi, Jun OhkuboDynamical Systems Koopman Operator Learning
Graduate School of Science and Engineering, Saitama University, Sakura, Saitama, 338-8570, Japan
Equation-Free Digital Twins for Nonlinear Structural Dynamics
May 1, 2026Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi +4Koopman Operator Learning Nonlinear System Identification
Department of Mechanical Engineering, Marine Technology and Arctic Group, Aalto University, Finland · Independent Expert, Digital Twin Technology, Ribe, Denmark · Institute for Risk and Reliability, Leibniz University Hannover, Germany
Inferring bifurcation diagrams of two distinct chaotic systems by a single machine
Apr 29, 2026Jianmin Guo, Yao Du, Yizhen Yu +2Dynamical Systems Nonlinear System Identification
School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710062, China · College of Physics and Electronic Information Engineering, Qinghai Normal University, Xining 810008, China · School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching
Apr 28, 2026Yuhe Bai, Chengli Tan, Jiaqi Li +2Dynamical Systems Regime-Switching Dynamical Systems
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, China · School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710072, China
Dictionary learning for Kernel EDMD
Apr 28, 2026Erik Lien Bolager, Boumediene Hamzi, Houman Owhadi +2Koopman Operator Learning Nonlinear System Identification
School of Computation, Information and Technology, Munich Data Science Institute, Munich Center for Machine Learning, Technical University of Munich, Germany · Department of Computing and Mathematical Sciences, Caltech, USA · The Alan Turing Institute, UK +1
Deep Learning for Model Calibration in Simulation of Itaconic Acid Production
Apr 24, 2026Daria Fokina, Marco Baldan, Constantin Romankiewicz +3Conditional Flow Matching Nonlinear System Identification
Fraunhofer ITWM, Kaiserslautern, Germany · RPTU Kaiserslautern, Kaiserslautern, Germany

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Learning dynamical systems with biochemically informed neural ordinary differential equations

Luis L. Fonseca, Reinhard C. Laubenbacher, Lucas Böttcher

Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA · Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, Frankfurt am Main, 60322, Germany

Dynamical Systems Neural Processes Nonlinear System Identification Neural ODEs

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Ordinary differential equation models of biochemical reactions are often formulated as stoichiometric systems in which the dynamics arise from a collection of interacting processes. A central challenge is that the functional form of each process is rarely known a priori and may be difficult to infer from data. We propose biochemically informed neural ordinary differential equations (BINODEs), a neural-ODE framework that retains the stoichiometric structure of mechanistic models while representing individual processes by neural networks. In BINODEs, the outputs of neural network processes (NNPs) are mapped to state derivatives through a linear layer analogous to a stoichiometric matrix. This architecture allows biological side information, such as process-specific inputs, sign constraints, and monotonicity assumptions, to be built directly into the model. We characterize the approximation properties of NNPs for several standard biochemical rate laws and show that the proposed framework recovers both trajectories and process-level structure in Monod, Lotka--Volterra, pharmacokinetic, and ultradian endocrine models. These results suggest that BINODEs offer a useful compromise between mechanistic interpretability and data-driven flexibility for modeling partially known biochemical or biological dynamical systems.

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