arxiv: 2604.14991 · v1 · submitted 2026-04-16 · 💻 cs.AI

Recognition: unknown

Predicting Power-System Dynamic Trajectories with Foundation Models

Haoran Li , Lihao Mai , Chenhan Xiao , Erik Blasch , Yang Weng

Authors on Pith no claims yet

Pith reviewed 2026-05-10 10:34 UTC · model grok-4.3

classification 💻 cs.AI

keywords power system dynamicstrajectory predictionfoundation modelsODE pretrainingzero-shot learningdynamic security analysisrenewable integration

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The pith

A foundation model pretrained on generic ODE trajectories predicts power-system dynamics from short prefixes in zero-shot settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a general framework for time-domain dynamic prediction in power systems undergoing transition to renewables. Existing methods lack generalization and require per-system training or data sharing, which hinders practical use for tasks like stability assessment. By pretraining on over 40 GB of trajectories from differential equations, the approach learns transferable representations that work across diverse regimes. Fine-tuning with 1 GB of power data and optimized computation for speed then allow accurate, private, and fast predictions. This supports operational needs without the usual barriers of customization or privacy issues.

Core claim

The proposed LASS-ODE-Power model, after pretraining on more than 40 GB of DAE and ODE trajectories and fine-tuning on approximately 1 GB of heterogeneous power-system data, supports accurate trajectory prediction from short measurement prefixes in zero-shot settings across electromechanical and inverter-driven systems, with fast inference enabled by parallel and linearized computation, and consistently outperforms existing learning-based models.

What carries the argument

LASS-ODE-Power framework that pretrains on large-scale generic DAE/ODE trajectories to learn transferable representations, combined with parallel linearized computation for inference speed.

Load-bearing premise

That the representations from pretraining on generic differential equation trajectories transfer to power-system dynamics with only limited fine-tuning and no system-specific parameters.

What would settle it

Demonstrating that on a power system with parameters or dynamics outside the fine-tuning distribution, the model's prediction accuracy falls below that of a system-specific trained model even after adaptation.

Figures

Figures reproduced from arXiv: 2604.14991 by Chenhan Xiao, Erik Blasch, Haoran Li, Lihao Mai, Yang Weng.

**Figure 1.** Figure 1: Top: Overview of the proposed framework, illustrating the pretraining and fine-tuning stages. Bottom: Architecture of LASS-ODE. Key components include GRU (Gated Recurrent Unit), RBF (Radial Basis Function), CSH (Common Structure Hub), Norm (Layer Normalization), MHA (Multi-Head Attention), and MoE (Mixture of Experts). • Find: Learn a general-purpose foundation model that, from a short observed prefix of … view at source ↗

**Figure 2.** Figure 2: Frequency trajectory prediction in the IEEE 39-bus system. The left two panels show over-frequency cases and the [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: Active-power trajectory prediction under post-event slow converter instability. All four cases use 40% of each [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Fault-driven trajectories with 60% observed in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

read the original abstract

As power systems transition toward renewable-rich and inverter-dominated operations, accurate time-domain dynamic analysis becomes increasingly critical. Such analysis supports key operational tasks, including transient stability assessment, dynamic security analysis, contingency screening, and post-fault trajectory evaluation. In practice, these tasks may operate under several challenges, including unknown and time-varying system parameters, privacy constraints on data sharing, and the need for fast online inference. Existing learning-based approaches are typically trained for individual systems and therefore lack generalization across operating conditions and physical parameters. Hence, this paper proposes LArge Scale Small ODE (LASS)-ODE-Power, a learning framework for general-purpose time-domain prediction. The proposed approach leverages large-scale pretraining on more than 40 GB of DAE or ordinary differential-equation (ODE) trajectories to learn transferable representations. The resulting model supports trajectory prediction from short measurement prefixes across diverse dynamic regimes, including electromechanical and inverter-driven systems. Hence, the model can be directly used without data sharing in a zero-shot setting. In addition, the proposed architecture incorporates parallel and linearized computation to achieve fast inference. Moreover, to enhance task-specific performance in power systems, a specialized fine-tuning strategy is developed based on approximately 1 GB of heterogeneous power-system dynamic data. Extensive experiments over diverse power-system simulation scenarios demonstrate that LASS-ODE-Power consistently outperforms existing learning-based models in trajectory prediction accuracy with efficient inference.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LASS-ODE-Power pretrains on large generic ODE data then fine-tunes lightly for zero-shot power-system trajectory prediction, but the transfer from unconstrained trajectories to constrained DAEs is the part that still needs verification.

read the letter

The main point to take away is that the authors pretrain on more than 40 GB of generic DAE and ODE trajectories, then fine-tune on roughly 1 GB of heterogeneous power-system data to produce a model that predicts trajectories from short measurement prefixes without per-system retraining or data sharing. This directly addresses the privacy and generalization bottlenecks that have kept earlier learning-based methods tied to individual systems. The parallel and linearized computation steps for faster inference are a reasonable engineering move for online applications. The paper also clearly states the operational needs around unknown parameters, inverter-dominated dynamics, and contingency screening, which helps ground the work in practice. What is new is the concrete scale of the pretraining combined with the power-system-specific fine-tuning pipeline and the claim of handling both electromechanical and inverter-driven regimes in a zero-shot setting. Earlier cited work is described as system-specific, so this combination appears fresh. The experiments are said to show consistent outperformance across diverse simulation scenarios, which is the kind of evidence that could matter if the numbers hold up. The soft spot is the transfer assumption itself. Power-system models are differential-algebraic equations with algebraic power-flow constraints and parameter-dependent nonlinearities that generic ODE trajectories are unlikely to encode on their own. If the 1 GB fine-tuning set does not adequately span topologies, parameters, and operating regimes, the zero-shot performance on unseen systems will degrade and the no-data-sharing benefit disappears. The abstract does not include quantitative metrics, error bars, or explicit baseline details, so it is hard to judge how large the gains are or whether post-hoc selection affected the results. A reader working on machine learning for power-system operations or on foundation models for scientific simulation would get value from the pipeline and the problem framing. I would send this to peer review. The idea is timely and the framework is laid out clearly enough that referees can test the transfer claims directly.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes LASS-ODE-Power, a foundation model for power-system dynamic trajectory prediction. It pretrains on >40 GB of generic DAE/ODE trajectories to learn transferable representations, then fine-tunes on ~1 GB of heterogeneous power-system data. The resulting model is claimed to enable accurate prediction from short measurement prefixes across electromechanical and inverter-driven regimes in a zero-shot manner without data sharing, while incorporating parallel and linearized computation for fast inference. Extensive experiments are asserted to show consistent outperformance over existing learning-based models.

Significance. If the central claims hold, the work would offer a practical advance for privacy-constrained, generalizable dynamic security analysis in renewable-rich power systems by reducing the need for per-system retraining. The large-scale pretraining strategy on generic trajectories is a notable strength for potential transferability. However, the assessed significance remains moderate because the transfer from unstructured ODE pretraining to algebraically constrained power-system DAEs is unverified and the reported results lack quantitative grounding.

major comments (3)

[Abstract] Abstract: The assertion of 'consistent outperformance' and 'efficient inference' across 'diverse power-system simulation scenarios' provides no quantitative metrics, error bars, baseline comparisons, or data-exclusion rules. This directly undermines verification of the central claim that the model supports trajectory prediction in a zero-shot setting.
[Abstract and §4] The zero-shot and no-data-sharing claim (Abstract and §4): The assertion that representations from generic DAE/ODE pretraining transfer to power-system dynamics (including algebraic power-flow constraints) after only ~1 GB heterogeneous fine-tuning lacks any ablation, topology diversity analysis, or tests on completely unseen systems. If the fine-tuning distribution does not cover relevant parameters and regimes, the zero-shot guarantee fails.
[§5] §5 (Experiments): No details are given on how the 1 GB fine-tuning set spans electromechanical versus inverter-driven regimes or on cross-system generalization metrics. Without these, the claim that the model 'can be directly used without data sharing in a zero-shot setting' cannot be evaluated and risks being limited to interpolation within the fine-tuning distribution.

minor comments (2)

[Abstract] The acronym expansion 'LArge Scale Small ODE (LASS)-ODE-Power' is introduced without clarifying the 'Small' component or its relation to the 40 GB pretraining scale.
[§3] The description of 'parallel and linearized computation' for fast inference lacks any complexity analysis, pseudocode, or comparison to standard ODE solvers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and detailed review. The comments highlight important areas for clarification and strengthening of the claims regarding quantitative support, transferability, and experimental details. We address each major comment below and have revised the manuscript to incorporate additional information and analyses where appropriate.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion of 'consistent outperformance' and 'efficient inference' across 'diverse power-system simulation scenarios' provides no quantitative metrics, error bars, baseline comparisons, or data-exclusion rules. This directly undermines verification of the central claim that the model supports trajectory prediction in a zero-shot setting.

Authors: We agree that the abstract, as a high-level summary, would be strengthened by including key quantitative indicators. In the revised manuscript, we have updated the abstract to report specific metrics such as average trajectory prediction error reductions (with standard deviations) relative to baselines, inference speedup factors, and the scope of evaluated scenarios. These are now cross-referenced to the detailed tables and figures in Section 5, while preserving the abstract's brevity. revision: yes
Referee: [Abstract and §4] The zero-shot and no-data-sharing claim (Abstract and §4): The assertion that representations from generic DAE/ODE pretraining transfer to power-system dynamics (including algebraic power-flow constraints) after only ~1 GB heterogeneous fine-tuning lacks any ablation, topology diversity analysis, or tests on completely unseen systems. If the fine-tuning distribution does not cover relevant parameters and regimes, the zero-shot guarantee fails.

Authors: This is a valid concern regarding the strength of evidence for transfer. The original manuscript presents results on heterogeneous fine-tuning data in Sections 4 and 5, but to directly address the request for ablations and unseen-system tests, we have added new experiments in the revision: (i) an ablation isolating the contribution of the 40 GB generic pretraining versus fine-tuning alone, (ii) topology diversity metrics across the fine-tuning set, and (iii) zero-shot evaluation on two completely held-out power-system models not represented in the fine-tuning distribution. These additions substantiate the transfer claims while clarifying the coverage of algebraic constraints. revision: yes
Referee: [§5] §5 (Experiments): No details are given on how the 1 GB fine-tuning set spans electromechanical versus inverter-driven regimes or on cross-system generalization metrics. Without these, the claim that the model 'can be directly used without data sharing in a zero-shot setting' cannot be evaluated and risks being limited to interpolation within the fine-tuning distribution.

Authors: We appreciate the request for greater transparency on dataset composition and generalization. In the revised Section 5, we have expanded the dataset description to quantify the distribution of the ~1 GB fine-tuning trajectories across electromechanical (e.g., synchronous machine) and inverter-driven regimes, including parameter ranges and operating conditions. We have also added explicit cross-system generalization results, reporting prediction accuracy on multiple held-out systems to demonstrate that performance extends beyond interpolation within the fine-tuning distribution and supports the zero-shot, no-data-sharing use case. revision: yes

Circularity Check

0 steps flagged

No significant circularity in pretrain-fine-tune pipeline

full rationale

The paper describes a standard empirical ML framework: large-scale pretraining on >40 GB generic DAE/ODE trajectories to learn representations, followed by ~1 GB heterogeneous fine-tuning for power-system adaptation, with experiments validating trajectory prediction accuracy and inference speed. Claims of zero-shot use without data sharing and transfer across electromechanical/inverter regimes are presented as empirical outcomes from the trained model, not as quantities that reduce by construction to fitted parameters, self-definitions, or self-citation chains. No load-bearing step matches self-definitional, fitted-input-renamed-as-prediction, or uniqueness-imported patterns; the derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on the transferability of learned representations from generic ODE trajectories to power-system DAEs and on the adequacy of the described fine-tuning procedure; these are not independently verified in the abstract.

free parameters (1)

foundation model architecture and hyperparameters
Specific network sizes, layers, and training hyperparameters are chosen to enable the claimed transfer but are not enumerated.

axioms (1)

domain assumption Large-scale pretraining on diverse ODE trajectories produces representations that generalize to power-system dynamics without explicit parameter knowledge.
Invoked in the description of the zero-shot setting and the motivation for pretraining on >40 GB of data.

invented entities (1)

LASS-ODE-Power model no independent evidence
purpose: General-purpose time-domain predictor for power-system trajectories
The named framework is introduced as the deliverable; no independent falsifiable handle (e.g., predicted physical constant) is provided.

pith-pipeline@v0.9.0 · 5550 in / 1457 out tokens · 45865 ms · 2026-05-10T10:34:58.858262+00:00 · methodology

discussion (0)

Reference graph

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