NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation

• URL: http://arxiv.org/abs/2405.14903v2
• Date: Thu, 31 Oct 2024 18:11:26 GMT
• Title: NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation
• Authors: Yifei Li, Yuchen Sun, Pingchuan Ma, Eftychios Sifakis, Tao Du, Bo Zhu, Wojciech Matusik,
• Abstract summary: We present a novel framework to explore neural control and design of complex fluidic systems with dynamic solid boundaries. Our system features a fast differentiable Navier-Stokes solver with solid-fluid interface handling. We present a benchmark of design, control, and learning tasks on high-fidelity, high-resolution dynamic fluid environments.
• Score: 36.0759668955729
• License:
• Abstract: We present a novel framework to explore neural control and design of complex fluidic systems with dynamic solid boundaries. Our system features a fast differentiable Navier-Stokes solver with solid-fluid interface handling, a low-dimensional differentiable parametric geometry representation, a control-shape co-design algorithm, and gym-like simulation environments to facilitate various fluidic control design applications. Additionally, we present a benchmark of design, control, and learning tasks on high-fidelity, high-resolution dynamic fluid environments that pose challenges for existing differentiable fluid simulators. These tasks include designing the control of artificial hearts, identifying robotic end-effector shapes, and controlling a fluid gate. By seamlessly incorporating our differentiable fluid simulator into a learning framework, we demonstrate successful design, control, and learning results that surpass gradient-free solutions in these benchmark tasks.

Related papers

• DualFluidNet: an Attention-based Dual-pipeline Network for FLuid Simulation [4.694954114339147]
  We propose an innovative approach for 3D fluid simulations utilizing an Attention-based Dual-pipeline Network. We find a way to achieve a better balance between global fluid control and physical law constraints. We also propose a new dataset, Tank3D, to further explore the network's ability to handle more complicated scenes.
  arXiv  Detail & Related papers  (2023-12-28T07:37:11Z)
• SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments [111.91255476270526]
  We introduce SoftZoo, a soft robot co-design platform for locomotion in diverse environments. SoftZoo supports an extensive, naturally-inspired material set, including the ability to simulate environments such as flat ground, desert, wetland, clay, ice, snow, shallow water, and ocean. It provides a variety of tasks relevant for soft robotics, including fast locomotion, agile turning, and path following, as well as differentiable design representations for morphology and control.
  arXiv  Detail & Related papers  (2023-03-16T17:59:50Z)
• FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation [80.63838153351804]
  We introduce FluidLab, a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. At the heart of our platform is a fully differentiable physics simulator, providing GPU-accelerated simulations and gradient calculations. We propose several domain-specific optimization schemes coupled with differentiable physics.
  arXiv  Detail & Related papers  (2023-03-04T07:24:22Z)
• Control of Two-way Coupled Fluid Systems with Differentiable Solvers [22.435002906710803]
  We investigate the use of deep neural networks to control complex nonlinear dynamical systems. We solve the Navier Stokes equations with two way coupling, which gives rise to nonlinear perturbations. We show that controllers trained with our approach outperform a variety of classical and learned alternatives in terms of evaluation metrics and generalization capabilities.
  arXiv  Detail & Related papers  (2022-06-01T09:12:08Z)
• Differentiable Simulation of Soft Multi-body Systems [99.4302215142673]
  We develop a top-down matrix assembly algorithm within Projective Dynamics. We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes.
  arXiv  Detail & Related papers  (2022-05-03T20:03:22Z)
• Physical Design using Differentiable Learned Simulators [9.380022457753938]
  In inverse design, learned forward simulators are combined with gradient-based design optimization. This framework produces high-quality designs by propagating through trajectories of hundreds of steps. Our results suggest that despite some remaining challenges, machine learning-based simulators are maturing to the point where they can support general-purpose design optimization.
  arXiv  Detail & Related papers  (2022-02-01T19:56:39Z)
• gradSim: Differentiable simulation for system identification and visuomotor control [66.37288629125996]
  We present gradSim, a framework that overcomes the dependence on 3D supervision by leveraging differentiable multiphysics simulation and differentiable rendering. Our unified graph enables learning in challenging visuomotor control tasks, without relying on state-based (3D) supervision.
  arXiv  Detail & Related papers  (2021-04-06T16:32:01Z)
• Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking [52.44199258132215]
  We present an open-source library of differentiable physics and robotics environments. The library features several popular environments, including classical control settings from OpenAI Gym. We give several use-cases of new scientific results obtained using the library.
  arXiv  Detail & Related papers  (2021-02-19T15:06:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.