title: Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time
summary: We propose a neural physics system for real-time, interactive fluid
  simulations. Traditional physics-based methods, while accurate, are
  computationally intensive and suffer from latency issues. Recent
  machine-learning methods reduce computational costs while preserving fidelity;
  yet most still fail to satisfy the latency constraints for real-time use and
  lack support for interactive applications. To bridge this gap, we introduce a
  novel hybrid method that integrates numerical simulation, neural physics, and
  generative control. Our neural physics jointly pursues low-latency simulation
  and high physical fidelity by employing a fallback safeguard to classical
  numerical solvers. Furthermore, we develop a diffusion-based controller that is
  trained using a reverse modeling strategy to generate external dynamic force
  fields for fluid manipulation. Our system demonstrates robust performance
  across diverse 2D/3D scenarios, material types, and obstacle interactions,
  achieving real-time simulations at high frame rates (11~29% latency) while
  enabling fluid control guided by user-friendly freehand sketches. We present a
  significant step towards practical, controllable, and physically plausible
  fluid simulations for real-time interactive applications. We promise to release
  both models and data upon acceptance.
paper_authors._id: 6835fb623d51d52701b0eaeb
paper_authors.name: Jingxuan Xu
paper_authors._id: 6835fb623d51d52701b0eaec
paper_authors.name: Hong Huang
paper_authors._id: 6835fb623d51d52701b0eaed
paper_authors.name: Chuhang Zou
paper_authors._id: 6835fb623d51d52701b0eaee
paper_authors.name: Manolis Savva
paper_authors._id: 6835fb623d51d52701b0eaef
paper_authors.name: Yunchao Wei
paper_authors._id: 6835fb623d51d52701b0eaf0
paper_authors.name: Wuyang Chen
paper_title: Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time
paper_summary: We propose a neural physics system for real-time, interactive fluid
  simulations. Traditional physics-based methods, while accurate, are
  computationally intensive and suffer from latency issues. Recent
  machine-learning methods reduce computational costs while preserving fidelity;
  yet most still fail to satisfy the latency constraints for real-time use and
  lack support for interactive applications. To bridge this gap, we introduce a
  novel hybrid method that integrates numerical simulation, neural physics, and
  generative control. Our neural physics jointly pursues low-latency simulation
  and high physical fidelity by employing a fallback safeguard to classical
  numerical solvers. Furthermore, we develop a diffusion-based controller that is
  trained using a reverse modeling strategy to generate external dynamic force
  fields for fluid manipulation. Our system demonstrates robust performance
  across diverse 2D/3D scenarios, material types, and obstacle interactions,
  achieving real-time simulations at high frame rates (11~29% latency) while
  enabling fluid control guided by user-friendly freehand sketches. We present a
  significant step towards practical, controllable, and physically plausible
  fluid simulations for real-time interactive applications. We promise to release
  both models and data upon acceptance.
paper_ai_summary: A hybrid neural physics system with diffusion-based control achieves real-time, interactive fluid simulations with low latency and high fidelity.
paper_ai_keywords: neural physics hybrid method numerical simulation real-time simulations diffusion-based controller reverse modeling strategy fluid manipulation high frame rates freehand sketches physically plausible fluid simulations