Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time
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.
6835fb623d51d52701b0eaeb
Jingxuan Xu
6835fb623d51d52701b0eaec
Hong Huang
6835fb623d51d52701b0eaed
Chuhang Zou
6835fb623d51d52701b0eaee
Manolis Savva
6835fb623d51d52701b0eaef
Yunchao Wei
6835fb623d51d52701b0eaf0
Wuyang Chen
Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time
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.
A hybrid neural physics system with diffusion-based control achieves real-time, interactive fluid simulations with low latency and high fidelity.
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