RheOFormer: A generative transformer model for simulation of complex fluids and flows

• URL: http://arxiv.org/abs/2510.01365v1
• Date: Wed, 01 Oct 2025 18:49:04 GMT
• Title: RheOFormer: A generative transformer model for simulation of complex fluids and flows
• Authors: Maedeh Saberi, Amir Barati Farimani, Safa Jamali,
• Abstract summary: Rheological Operator Transformer (RheOFormer) is a generative operator learning method leveraging self-attention to efficiently learn different spatial interactions and features of complex fluid flows.<n>Our results demonstrate that RheOFormer can accurately learn both scalar and tensorial nonlinear mechanics of different complex fluids predict the evolution of their flows, even when trained on limited datasets.
• Score: 10.21291474099901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to model mechanics of soft materials under flowing conditions is key in designing and engineering processes and materials with targeted properties. This generally requires solution of internal stress tensor, related to the deformation tensor through nonlinear and history-dependent constitutive models. Traditional numerical methods for non-Newtonian fluid dynamics often suffer from prohibitive computational demands and poor scalability to new problem instances. Developments in data-driven methods have mitigated some limitations but still require retraining across varied physical conditions. In this work, we introduce Rheological Operator Transformer (RheOFormer), a generative operator learning method leveraging self-attention to efficiently learn different spatial interactions and features of complex fluid flows. We benchmark RheOFormer across a range of different viscometric and non-viscometric flows with different types of viscoelastic and elastoviscoplastic mechanics in complex domains against ground truth solutions. Our results demonstrate that RheOFormer can accurately learn both scalar and tensorial nonlinear mechanics of different complex fluids and predict the spatio-temporal evolution of their flows, even when trained on limited datasets. Its strong generalization capabilities and computational efficiency establish RheOFormer as a robust neural surrogate for accelerating predictive complex fluid simulations, advancing data-driven experimentation, and enabling real-time process optimization across a wide range of applications.

Related papers

• Curly Flow Matching for Learning Non-gradient Field Dynamics [49.480209466896035]
  We introduce Curly Flow Matching (Curly-FM), a novel approach to learning non-gradient field dynamics.<n>Curly-FM is capable of learning non-gradient field dynamics by designing and solving a Schr"odinger bridge problem.<n>Curly-FM can learn trajectories that better match both the reference process and population marginals.
  arXiv  Detail & Related papers  (2025-10-30T16:11:39Z)
• No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs [56.78271181959529]
  This paper proposes a conceptual shift to modeling low-dimensional dynamical systems by departing from the traditional two-step modeling process.<n>Instead of first discovering a closed-form equation and then analyzing it, our approach, direct semantic modeling, predicts the semantic representation of the dynamical system.<n>Our approach not only simplifies the modeling pipeline but also enhances the transparency and flexibility of the resulting models.
  arXiv  Detail & Related papers  (2025-01-30T18:36:48Z)
• Physics-enhanced Neural Operator for Simulating Turbulent Transport [9.923888452768919]
  This paper presents a physics-enhanced neural operator (PENO) that incorporates physical knowledge of partial differential equations (PDEs) to accurately model flow dynamics. The proposed method is evaluated through its performance on two distinct sets of 3D turbulent flow data.
  arXiv  Detail & Related papers  (2024-05-31T20:05:17Z)
• Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with Neural Networks [0.0]
  The proposed model architecture is simpler and more efficient compared to existing solutions from the literature. The validation of the approach is carried out by means of surrogate data obtained with the Armstrong-Frederick kinematic hardening model.
  arXiv  Detail & Related papers  (2024-03-04T07:09:54Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Machine learning of hidden variables in multiscale fluid simulation [77.34726150561087]
  Solving fluid dynamics equations often requires the use of closure relations that account for missing microphysics. In our study, a partial differential equation simulator that is end-to-end differentiable is used to train judiciously placed neural networks. We show that this method enables an equation based approach to reproduce non-linear, large Knudsen number plasma physics.
  arXiv  Detail & Related papers  (2023-06-19T06:02:53Z)
• Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes [37.69303106863453]
  We present a hybrid quantum physics-informed neural network that simulates laminar fluid flows in 3D Y-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a physics-informed neural network, resulting in a 21% higher accuracy compared to a purely classical neural network.
  arXiv  Detail & Related papers  (2023-04-21T20:49:29Z)
• Scientific Machine Learning for Modeling and Simulating Complex Fluids [0.0]
  rheological equations relate internal stresses and deformations in complex fluids. Data-driven models provide accessible alternatives to expensive first-principles models. Development of similar models for complex fluids has lagged.
  arXiv  Detail & Related papers  (2022-10-10T04:35:31Z)
• Evolution TANN and the discovery of the internal variables and evolution equations in solid mechanics [0.0]
  We propose a new approach which allows, for the first time, to decouple the material representation from the incremental formulation. Inspired by the Thermodynamics-based Artificial Neural Networks (TANN) and the theory of the internal variables, the evolution TANN (eTANN) are continuous-time. Key feature of the proposed approach is the discovery of the evolution equations of the internal variables in the form of ordinary differential equations.
  arXiv  Detail & Related papers  (2022-09-27T09:25:55Z)
• Equivariant vector field network for many-body system modeling [65.22203086172019]
  Equivariant Vector Field Network (EVFN) is built on a novel equivariant basis and the associated scalarization and vectorization layers. We evaluate our method on predicting trajectories of simulated Newton mechanics systems with both full and partially observed data.
  arXiv  Detail & Related papers  (2021-10-26T14:26:25Z)
• A Gradient-based Deep Neural Network Model for Simulating Multiphase Flow in Porous Media [1.5791732557395552]
  We describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses.
  arXiv  Detail & Related papers  (2021-04-30T02:14:00Z)
• Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer [56.34005280792013]
  We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
  arXiv  Detail & Related papers  (2021-01-19T16:14:02Z)

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.