Hamiltonian Dynamics

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 32

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Out-of-control Hamiltonian Learning
Oct 5, 2026Weiyuan Gong, Muzhou Ma, Sitan Chen +2Quantum Many-Body Physics Hamiltonian Dynamics
Harvard University. · Caltech. · Oratomic.
Elucidating the Conformal Structure of the Brinkman Penalisation Method for Geometry-Adapted, Structure-Preserving Operator Learning of Hamiltonian PDEs
Sep 24, 2026Teo Deveney, Baige Xu, Takaharu YaguchiPDE Operator Learning Neural Operators
Department of Computer Science, University of Bath, United Kingdom · Center for Advanced Intelligence Project, RIKEN, Japan · Kyushu University / Center for Advanced Intelligence Project, RIKEN, Japan
A matched-integrator evaluation of Hamiltonian neural networks on pendulum and Kepler dynamics
Aug 10, 2026Lenick Kemunto Nyabuto, Yae Ulrich Gaba, Birahim TeweDynamical Systems Hamiltonian Neural Networks
African Institute for Mathematical Sciences (AIMS) Senegal
Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks
Jul 31, 2026Jaesung ChoiNeural Surrogate Modeling Dynamical Systems
Center for Artificial Intelligence and Natural Sciences, Korea Institute for Advanced Study, Seoul 02455, Korea
Latent Lie-Poisson Neural Networks (LLPNNs): Discovering the motion of Lie-Poisson systems through observable data and latent dynamics
Jul 31, 2026Vakhtang PutkaradzeLatent Dynamics Modeling Hamiltonian Neural Networks
Department of Mathematics, The University of Alabama, Gordon Palmer Hall, Tuscaloosa, AL, 35401, USA
When Rates Are Geometric: Rate-Certificate Transfer for Contact Splittings in Optimization
Jul 26, 2026George A KevrekidisOptimization Convergence Analysis Hamiltonian Dynamics
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
Dynamical and Optimization Trade-offs of Levi--Civita Coordinates for Learned Close-Encounter Dynamics
Jul 22, 2026Abhishek ShankarResidual Learning Hamiltonian Dynamics
Quantum Port-Hamiltonian Neural Networks: Learning Conservative and Dissipative Dynamics via Measurement-Induced Nonlinearity
Jul 14, 2026Dibakar SigdelHamiltonian Neural Networks Port-Hamiltonian Systems
1Mindverse Computing LLC, Lynnwood, WA 98087
Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models
Jul 8, 2026Eli Laird, Corey ClarkPhysical Consistency in Video Generation Latent Dynamics Modeling
Department of Computer Science, Southern Methodist University, Dallas, TX, USA
CSympNet-ID: conformal-symplectic map learning for linearly damped Hamiltonian systems
Jul 3, 2026Jiale Gong, Pengzhan Jin, Dongyang Kuang +2Dynamical Systems Nonlinear System Identification
School of Mathematics · School of Mathematics (Zhuhai), Sun Yat-sen University, Zhuhai 519082, China · National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China +3
Compositional Dynamics in Learning and Mechanics
Jun 27, 2026David I. SpivakDynamical Systems Hamiltonian Dynamics
Symplectic Neural Networks for learning Generalized Hamiltonians
Jun 25, 2026Harsh Choudhary, Vyacheslav Kungurtsev, Chandan Gupta +2Dynamical Systems Hamiltonian Neural Networks
Computer Science Department, Indraprasth Institute of Information Technology, New Delhi, India 110020 · Athena Research and Innovation Center, Marousi, Greece 15125 · HSBC Holdings, Singapore
When Do Conservation Laws Survive Learned Representations? Certified Horizons for Latent World Models
Jun 23, 2026Hongbo WangDynamical Systems Latent World Models
Department of Mathematics, Stony Brook University, Stony Brook, NY 11794, USA
Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time
Jun 15, 2026Xiuyuan Wang, Vishwak Srinivasan, Qiang Fu +3Hamiltonian Dynamics
Department of Computer Science, Yale University · Department of EECS, Massachusetts Institute of Technology
Learning symplectic model reduction based on a approximation theorem of symplectic embeddings
Jun 3, 2026Liyi Feng, Yifa Tang, Yulin Xie +2Dynamical Systems Autoencoders
School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China · State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China · Department of Mathematics, National University of Singapore, 119076, Singapore
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
Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction
May 19, 2026Robert Jenkinson AlvarezJoint-Embedding Predictive Architecture Geometric Representation Learning
PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics
May 18, 2026Xueyu Luan, Chenwei ShiDynamical Systems Reinforcement Learning
School of Electronic and Information Engineering Tongji University Shanghai 200092, China · Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai 200092, China
Prediction Is Not Physics: Learning and Evaluating Conserved Quantities in Neural Simulators
May 16, 2026Andrew Bukowski, Aditya Kothari, Simba Shi +1Neural Surrogate Modeling Dynamical Systems
Yale University
Identify Then Project: Contrastive Learning of Latent Dynamics from Partial Observations with Port-Hamiltonian Structure
May 15, 2026Peilun Li, Kaiyuan Tan, Daniel Moyer +1Contrastive Learning Latent Dynamics Modeling
Department of Computer Science Vanderbilt University Nashville, TN 37235
Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems
May 15, 2026Yeang Makara, Yusuke Tanaka, Takashi Matsubara +1PDE Operator Learning Neural Operators
Graduate School of Science Kobe University 1-1 Rokkodai-cho, Nada-ku, Kobe, Japan · NTT Communication Science Laboratories 2-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan · Faculty of Information Science and Technology Hokkaido University Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido, Japan +1
Physics Guided Generative Optimization for Trotter Suzuki Decomposition
May 13, 2026WenBin YanQuantum Circuit Compilation Hamiltonian Dynamics
University of Colorado Boulder[quant-ph]
When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
May 7, 2026Yi Wang, Chandrajit BajajDynamical Systems Learning to Optimize
1Oden Institute · Department of Computer Science
HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning
May 7, 2026Haoyun Tang, Haodong Cui, Keyao Xu +2World Model Learning Latent Dynamics Modeling
1Xi’an Jiaotong University · 2Huazhong University of Science and Technology · 3Nankai University +1
Detecting Deepfakes via Hamiltonian Dynamics
May 6, 2026Harry Cheng, Ming-Hui Liu, Tianyi Wang +3Digital Image Forensics Deepfake Detection
School of Computing, National University of Singapore, Singapore · School of Software, Shandong University, China · School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), China +1
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
May 1, 2026Sen Cui, Jingheng MaLatent Dynamics Modeling World Models
Tsinghua University
Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data
Apr 26, 2026Ru Geng, Panayotis Kevrekidis, Yixian Gao +2Dynamical Systems Graph Structure Learning
School of Mathematics and Statistics, Changchun University, Changchun, 130022, China · Department of Mathematics and Statistics, University of Massachusetts, Amherst, 01003, MA, USA · Department of Physics, University of Massachusetts, Amherst, 01003, MA, USA +2
Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data
Apr 19, 2026Vedanta Thapar, Abhinav GuptaNonlinear System Identification Recurrent Neural Networks
The Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom · Center for Theoretical Physics, St. Stephen’s College, University of Delhi, Delhi 110007, India
Early-Terminable Energy-Safe Iterative Coupling for Parallel Simulation of Partitioned Port-Hamiltonian Systems
Mar 17, 2026Qi Wei, Jianfeng Tao, Hongyu Nie +1Dynamical Systems Port-Hamiltonian Systems
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
Jan 29, 2026Winfried Ripken, Michael Plainer, Gregor Lied +5Dynamical Systems Flow Map Learning
Technical University Berlin · BIFOLD · Free University of Berlin +6

Oct 5, 2026, quant-phJ/K move · Enter open · S save

Out-of-control Hamiltonian Learning

Weiyuan Gong, Muzhou Ma, Sitan Chen, Jordan Cotler+1

Harvard University. · Caltech. · Oratomic.

Quantum Many-Body Physics Hamiltonian Dynamics

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Learning the Hamiltonian of a many-body system from its dynamics is a central task in quantum science, yet the algorithms with the strongest provable guarantees assume some level of quantum control--fast, arbitrary single-qubit gates interleaved with time evolution, and measurements in arbitrary bases--that is beyond the capabilities of near-term analog quantum simulators. Motivated by analog atom- and ion-based platforms, we study Hamiltonian learning under minimal access models. Uniform state preparation and measurements: We first consider the setting where in every experiment, one can rotate each qubit to the same state, perform short-time evolution, and measure every qubit in the same basis. Surprisingly, we show that for generic 2-local Hamiltonians on any interaction graph, all of the parameters can be reconstructed from such experiments. Computational basis state preparation and measurements: We then consider a similarly constrained setting, but where state preparation and measurement are restricted to the computational basis. For nearest-neighbor Hamiltonians with only Pauli

X/Z

interactions, a class which captures contemporary Rydberg atom platforms, we show that over 1D and 2D rectangular lattices, all of the parameters can be reconstructed from such experiments up to unavoidable gauges. Our protocols introduce new techniques for solving structured polynomial systems over an extensive number of parameters. Taken together, our results suggest that one can learn a great deal from the dynamics of quantum many-body systems even under the most stringent experimental constraints.

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Latest papers 32