Neural ODEs

ODE: Ordinary Differential Equation

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Learning to Solve Generative ODEs Beyond the Linear Span
Jun 7, 2026Sihyeon Kim, Seunghun Lee, Vikas Singh +1Few-Step Diffusion Sampling Flow-Based Generative Modeling
Korea University · KAIST · University of Wisconsin–Madison
Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction
Jun 4, 2026Jaeyeong Lee, Wonmo Koo, Heeyoung KimBayesian Neural Networks Probabilistic Forecasting
aDepartment of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Learning Manifold and Itô Dynamics with Branched Neural Rough Differential Equations
Jun 3, 2026Luke Thompson, Dai Shi, Lequan Lin +2Dynamical Systems Stochastic Differential Equations
University of Sydney, Australia · Cambridge University, United Kingdom.
Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
Jun 1, 2026Marah Almanasreh, Alexander Mitsos, Eike CramerNeural Surrogate Modeling Materials Science
RWTH Aachen University, Process Systems Engineering (AVT.SVT), 52074 Aachen, Germany · JARA-CSD, 52425 Jülich, Germany · Energy Systems Engineering (ICE-1), Forschungszentrum Jülich, 52425 Jülich, Germany +1
Faithful Embeddings of Irregular and Asynchronous Data for Online Log-NCDEs
May 28, 2026Benjamin Walker, Alexandre Bloch, Lingyi Yang +2Irregular Time-Series Modeling Neural Controlled Differential Equations
Mathematical Institute University of Oxford · School of Mathematical Sciences University of Nottingham · Department of Mathematics, Imperial College London
Universal Time Series Generation with Neural Controlled Differential Equations
May 27, 2026Torben Berndt, Elyes Farjallah, Leif Seute +3Neural Controlled Differential Equations Time Series Generation
Heidelberg Institute for Theoretical Studies, Heidelberg, Germany · IAR, Karlsruhe Institute of Technology, Karlsruhe, Germany · Max Planck Institute for Polymer Research, Mainz, Germany +4
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
BrainDyn: A Sheaf Neural ODE for Generative Brain Dynamics
May 19, 2026Siddharth Viswanath, Panayiotis Ketonis, Chen Liu +3Graph Neural Networks Sheaf Neural Networks
1Yale University · 2Boise State University · University of Wisconsin–Madison
Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations
May 13, 2026Phillip Si, Yuan Qiu, Omar Sallam +4Neural Surrogate Modeling PDE Surrogate Modeling
School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street Northwest, Atlanta, 30308, GA, USA · Environmental Science Division, Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
May 13, 2026Lake Yang, Antonio Malpica-Morales, Frank Ioannis Papadakis Wood +1Neural Network Generalization System Identification
Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
Steerable Neural ODEs on Homogeneous Spaces
May 11, 2026Emma Andersdotter, Daniel Persson, Fredrik OhlssonEquivariant Neural Networks Geometric Representation Learning
Department of Mathematics and Mathematical Statistics Umeå University Umeå SE-901 87, Sweden · Department of Mathematical Sciences2026 Chalmers University of Technology and Gothenburg University Gothenburg SE-412 96, Sweden · Department of Mathematics and Mathematical Statistics 11 Umeå University Umeå SE-901 87, Sweden
Exact Fixed-Point Constraints in Neural-ODEs with Provable Universality
May 11, 2026Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni +3Constrained Optimization Neural Network Approximation Theory
Department of Informatics and Computer Science, University of Pisa, Italy · Department of Physics and Astronomy and INFN, University of Florence, Sesto Fiorentino, Italy
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
May 6, 2026Becky Langdon, Gabriel D. Patrón, Chrysoula D. Kappatou +6Meta-Learning Bayesian Optimization
Department of Computing, Imperial College London, United Kingdom · BASF SE, Ludwigshafen, Germany · School of Mathematical Sciences, Lancaster University, United Kingdom +1
Physics-Modeled Neural Networks
May 5, 2026Raul Felipe-Sosa, Angel Martin del Rey, Maria Flores CeballosDynamic Neural Networks Neural ODEs
aDepartamento de Matemáticas, Escuela Politénica Superior, Universidad Nebrija, 28015 Madrid, Spain · bDepartment of Applied Mathematics, IUFFyM, Universidad de Salamanca, 37008 Salamanca, Spain · cUnidad de Excelencia en Luz y Materia Estructuradas (LUMES), Universidad de Salamanca, 37008 Salamanca, Spain
Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
May 5, 2026Xiao Zhang, Yafei Li, Ruixiang Wang +3Spatiotemporal Forecasting Neural ODEs
Zhengzhou University · Northwestern Polytechnical University
Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach
May 5, 2026Shugen Song, Wenjie Mei, Chengyan ZhaoNonlinear MPC Residual Learning
School of Automation, Southeast University, Nanjing 210096, China · School of Automation and the Key Laboratory of MCCSE of the Ministry of Education, Southeast University, Nanjing 210096, China · Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi, Fukuoka, Japan
PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
Apr 30, 2026Hira Saleem, Flora Salim, Cormac PurcellTransformer Physics-Informed ML
University of New South Wales, Sydney, NSW, Australia
Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time
Apr 28, 2026Jennifer Wendland, Nicolas Freitag, Maik KschischoCausal Effect Estimation Clinical Outcome Prediction
Department of Computer Science University of Koblenz 53424 Koblenz, Germany
Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions
Apr 27, 2026Qinhan Hou, Jing TangDynamical Systems Graph Structure Learning
Doctoral Program of Computer Science, University of Helsinki, Helsinki, Finland · Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning
Apr 27, 2026Xiaoyi Wang, Zhiqiang Wang, Jianqing Liang +4Temporal GNNs Graph Representation Learning
Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China. · Department of System Engineering and Engineering Management, City University of Hong Kong, Kowloon 999077, Hong Kong, China. · Key Laboratory of Complex Systems and Data Science of Ministry of Education, Complex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China.
Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems
Apr 20, 2026Hanfeng Zhai, Hongtao Qiao, Hassan Mansour +1Neural Surrogate Modeling Physics-Informed ML
Department of Mechanical Engineering, Stanford University, Stanford, CA · Mitsubishi Electric Research Laboratories, Cambridge, MA
Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
Apr 3, 2026Dechuan Liu, Ruigang Wang, Ian R. ManchesterConstrained Motion Planning Safe Motion Planning
Australian Centre for Robotics (ACFR), and the School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia
A convolutional autoencoder and neural ODE surrogate modeling framework applied to transient counterflow flames
Mar 16, 2026Mert Yakup Baykan, Weitao Liu, Mohammad Rafi Malik +4Autoencoders PDE Surrogate Modeling
Department of Aerospace Engineering, KAIST, Daejeon, Republic of Korea · Clean Energy Research Platform (CERP), KAUST, Thuwal, Saudi Arabia · Institute for Reactive Flows (IRST), University of Stuttgart, Pfaffenwaldring 31, 70569 Stuttgart, Germany
How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting
Feb 6, 2026Yiqi Su, Ray Lee, Jiaming Cui +1Epidemic Forecasting Non-Stationary Time Series Forecasting
Department of Computer Science, Virginia Tech, Alexandria, USA.
Geometry-Preserving Neural Architectures on Manifolds with Boundary
Feb 3, 2026Karthik Elamvazhuthi, Shiba Biswal, Kian Rosenblum +4Geometric Representation Learning Neural Network Approximation Theory
Los Alamos National Laboratory · Boston College
Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
Feb 2, 2026Egor Serov, Ilya Kuleshov, Alexey ZaytsevEfficient Neural Network Inference Neural Controlled Differential Equations
Applied AI Institute · Risk AI Research Lab
Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
Jan 20, 2026Xu Zhang, Junwei Deng, Chang Xu +2Irregular Time-Series Modeling Neural Controlled Differential Equations
Microsoft Research, Beijing, China · University of Illinois Urbana-Champaign, Illinois, United States · Imperial College London, London, United Kingdom
FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
Nov 13, 2025YongKyung Oh, Dong-Young Lim, Sungil KimIrregular Time-Series Modeling Time Series Classification
Medical & Imaging Informatics (MII) Group, University of California, Los Angeles (UCLA), CA, USA · Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea · Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea

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Learning to Solve Generative ODEs Beyond the Linear Span

Sihyeon Kim, Seunghun Lee, Vikas Singh, Hyunwoo J. Kim

Korea University · KAIST · University of Wisconsin–Madison

Few-Step Diffusion Sampling Flow-Based Generative Modeling Neural ODEs Diffusion Model Inference Acceleration

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Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations. Solver learning reduces this cost by adapting scalar coefficients, timesteps, or both, while keeping the backbone model fixed. In this work, we identify a structural bottleneck in this update family: each step remains span-limited. Since the scalar-coefficient update lies in the span of buffered velocity evaluations, it can fit only the in-span component while leaving any out-of-span residual unreachable by scalar recombination alone. We propose SpanLift, a lightweight neural solver that augments scalar-coefficient updates with a spatial residual operator. SpanLift keeps a fixed base solver as an in-span prior and learns a spatial residual operator over the state and velocity buffer. The operator is trained by endpoint teacher matching, preserves the pretrained backbone, and adds no model NFEs. Empirically, the learned correction transfers across base solvers and is predominantly out-of-span. Across pixel-space diffusion, latent flow matching, and precipitation nowcasting, SpanLift achieves state-of-the-art few-step sampling. With only 3 NFE, it improves CIFAR-10 FID from 8.16 to 5.69 and ImageNet FID from 17.37 to 11.83.

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