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ARXIV:2603.05598 · PHYSICS FOUNDATION MODELS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.05598PHYSICS FOUNDATION MODELSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Pretraining tokenizers for physics foundation models significantly improves efficiency and accuracy in physics emulation, offering a practical approach for building domain-specific emulators.
Opportunity summary
Pain Pretraining tokenizers for physics foundation models significantly improves efficiency and accuracy in physics emulation, offering a practical approach for building domain-specific emulators.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Pretraining tokenizers for physics foundation models significantly improves efficiency and accuracy in physics emulation, offering a practical approach for building domain-specific emulators. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes…
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Training foundation models to learn the dynamics underlying such data enables the modelling of complex multiphysics phenomena, especially in data-limited settings.
Physics Foundation Models moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Pretraining tokenizers for physics foundation models significantly improves efficiency and accuracy in physics emulation, offering a practical approach for building domain-specific emulators.
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10.48550/arXiv.2603.05598Pretraining tokenizers for physics foundation models significantly improves efficiency and accuracy in physics emulation, offering a practical approach for building domain-specific emulators.
Abstract
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales. Training foundation models to learn the dynamics underlying such data enables the modelling of complex multiphysics phenomena, especially in data-limited settings. The emerging class of physics foundation models typically aims to learn two tasks jointly: (i) extracting compact representations of high-resolution spatiotemporal data, and (ii) capturing governing physical dynamics. However, learning both tasks from scratch simultaneously can impede the effectiveness of either process. We demonstrate that pretraining the tokeniser with an autoencoding objective prior to training the dynamics model enhances computational efficiency for downstream tasks. Notably, the magnitude of this benefit depends on domain alignment: pretraining on the same physical system as the downstream task yields the largest improvements, while pretraining on other systems provides moderate gains. In-domain pretraining reduces VRMSE by 64% after 10,500 training steps compared to training from scratch. To our knowledge, this is the first systematic investigation of tokeniser pretraining for physics foundation models. We further introduce flexible spatiotemporal compression operations that extend causal convolutions to support runtime-adjustable compression ratios, enabling efficient adaptation to diverse downstream tasks. Our findings provide practical guidance for training efficient physics emulators and highlight the importance of strategic pretraining data selection.
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Dimensions overall score 7.0
PROBLEM
Pretraining tokenizers for physics foundation models significantly improves efficiency and accuracy in physics emulation, offering a practical approach for building domain-specific emulators. Modern high-resolution simulations produce vast volumes of data spanning diverse physic...
METHOD
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Training foundation models to learn the dynamics underlying such data enables the modelling of complex multiphysics phenomena, especially in data-limited settings.
WHY NOW
Physics Foundation Models moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Pretraining tokenizers for physics foundation models significantly improves efficiency and accuracy in physics emulation, offering a practical approach for building domain-specific emulators. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Training foundation models to learn the dynamics underlying such data enables the modelling of complex multiphysics phenomena, especially in data-limited settings.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Physics Foundation Models moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Pretraining tokenizers for physics foundation models significantly improves efficiency and accuracy in physics emulation, offering a practical approach for building domain-specific emulators.
Segment
Physics Foundation Models
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