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EMMA framework extracts physical parameters from multimodal data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EMMA, a novel physics-informed framework capable of extracting multiple physical parameters from multimodal data, including video, audio, and chart-based time-series observations. This approach overcomes limitations of prior video-only methods by jointly inferring explicit parameters, latent dynamics, and calibration invariants within a continuous-time model. EMMA utilizes a Liquid Time-Constant network and a physics-constrained loss to ensure consistency with governing differential equations, demonstrating robust multi-parameter recovery across diverse scenarios and outperforming existing baselines. AI

IMPACT Introduces a new method for physics-consistent model extraction from multimodal data, potentially improving scientific modeling and simulation.

RANK_REASON The cluster contains a research paper detailing a new framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

EMMA framework extracts physical parameters from multimodal data

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The cluster contains a research paper detailing a new framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Farhat Shaikh, Ayan Banerjee, Sandeep Gupta ·

    EMMA: Extracting Multiple physical parameters from Multimodal Data

    arXiv:2605.24047v1 Announce Type: new Abstract: We introduce EMMA, a physics-informed multimodal framework that recovers all identifiable dynamical parameters of a system directly from raw video, audio, and image-based time-series observations. Unlike prior video-only approaches …

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