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[2602.17998] PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting
Summary
The paper presents PHAST, a Port-Hamiltonian architecture designed for forecasting dynamics in physical systems using only position data, achieving superior long-horizon predictions and parameter recovery.
Why It Matters
This research addresses a critical challenge in scientific machine learning: forecasting the dynamics of real-world systems from limited observations. By introducing PHAST, the authors provide a structured approach that enhances forecasting accuracy and identifies physical parameters, which is essential for applications in various scientific fields.
Key Takeaways
- PHAST effectively forecasts dynamics using only position data.
- The architecture separates conservative and dissipative dynamics for better stability.
- It demonstrates superior performance across multiple benchmarks in various domains.
- Parameter recovery is enhanced when sufficient structural information is available.
- The study highlights the importance of anchors for identifying system dynamics.
Computer Science > Machine Learning arXiv:2602.17998 (cs) [Submitted on 20 Feb 2026] Title:PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting Authors:Shubham Bhardwaj, Chandrajit Bajaj View a PDF of the paper titled PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting, by Shubham Bhardwaj and 1 other authors View PDF Abstract:Real physical systems are dissipative -- a pendulum slows, a circuit loses charge to heat -- and forecasting their dynamics from partial observations is a central challenge in scientific machine learning. We address the \emph{position-only} (q-only) problem: given only generalized positions~$q_t$ at discrete times (momenta~$p_t$ latent), learn a structured model that (a)~produces stable long-horizon forecasts and (b)~recovers physically meaningful parameters when sufficient structure is provided. The port-Hamiltonian framework makes the conservative-dissipative split explicit via $\dot{x}=(J-R)\nabla H(x)$, guaranteeing $dH/dt\le 0$ when $R\succeq 0$. We introduce \textbf{PHAST} (Port-Hamiltonian Architecture for Structured Temporal dynamics), which decomposes the Hamiltonian into potential~$V(q)$, mass~$M(q)$, and damping~$D(q)$ across three knowledge regimes (KNOWN, PARTIAL, UNKNOWN), uses efficient low-rank PSD/SPD parameterizations, and advances dynamics with Strang splitting. Across thirteen q-only benchmarks spanning mechanical, electrical, molecular, thermal, gravitational, and ecologi...
