mlsanityData to 14 Oct 2026

DamFormer: Generalizing Morphologies in Dam Break Simulations Using Transformer Model

arXiv 2410.18998 ↗First posted 17 Oct 2024physics.flu-dyn cs.LGcited by 0

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Abstract (arXiv)

The interaction of waves with structural barriers such as dams breaking plays a critical role in flood defense and tsunami disasters. In this work, we explore the dynamic changes in wave surfaces impacting various structural shapes, e.g., circle, triangle, and square, by using deep learning techniques. We introduce the DamFormer, a novel transformer-based model designed to learn and simulate these complex interactions. The model was trained and tested on simulated data representing the three structural forms.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 34 references that are in the map. First posted on arXiv 17 Oct 2024.

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References · 5

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Machine Learning for Fluid Mechanicscited by 385
  3. 2023Neural Operators for Accelerating Scientific Simulations and Designcited by 221
  4. 2024Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operatorscited by 27
  5. 2020Physics guided machine learning using simplified theoriescited by 23

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