Machine Learning-Integrated Hybrid Fluid-Kinetic Framework for Quantum Electrodynamic Laser Plasma Simulations

• URL: http://arxiv.org/abs/2510.11174v1
• Date: Mon, 13 Oct 2025 09:07:59 GMT
• Title: Machine Learning-Integrated Hybrid Fluid-Kinetic Framework for Quantum Electrodynamic Laser Plasma Simulations
• Authors: Sadra Saremi, Amirhossein Ahmadkhan Kordbacheh,
• Abstract summary: The research introduces a machine learning-based three-dimensional hybrid fluid-particle-in-cell (PIC) system.<n>The technique employs fluid approximations for stable areas but activates the PIC solver when SwitchNet directs it to unstable sections.<n>The model produces precise predictions with coefficient of determination (R2) values above 0.95 and mean squared errors below 10-4 for all field components.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-intensity laser plasma interactions create complex computational problems because they involve both fluid and kinetic regimes, which need models that maintain physical precision while keeping computational speed. The research introduces a machine learning-based three-dimensional hybrid fluid-particle-in-cell (PIC) system, which links relativistic plasma behavior to automatic regime transitions. The technique employs fluid approximations for stable areas but activates the PIC solver when SwitchNet directs it to unstable sections through its training on physics-based synthetic data. The model uses a smooth transition between Ammosov-Delone-Krainov (ADK) tunneling and multiphoton ionization rates to simulate ionization, while Airy-function approximations simulate quantum electrodynamic (QED) effects for radiation reaction and pair production. The convolutional neural network applies energy conservation through physics-based loss functions, which operate on normalized fields per channel. Monte Carlo dropout provides uncertainty measurement. The hybrid model produces precise predictions with coefficient of determination (R^2) values above 0.95 and mean squared errors below 10^-4 for all field components. This adaptive approach enhances the accuracy and scalability of laser-plasma simulations, providing a unified predictive framework for high-energy-density and particle acceleration applications.

Related papers

• Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulations [3.7612198046810814]
  Hybrid-Vlasov simulations resolve ion-kinetic effects for modeling the solar wind-magnetosphere interaction.<n>We show that graph-based machine learning emulators can learn the evolution of electromagnetic fields and lower order moments of ion velocity distribution.
  arXiv  Detail & Related papers  (2026-01-18T23:13:23Z)
• Hybrid Fourier Neural Operator-Plasma Fluid Model for Fast and Accurate Multiscale Simulations of High Power Microwave Breakdown [2.202064335120138]
  We present a hybrid modeling approach that combines the accuracy of a differential equation-based plasma fluid solver with the computational efficiency of FNO.<n>Trained on data from an in-houseD-based plasma-fluid solver, the FNO replaces computationally expensive EM field updates.<n>Our work also demonstrate how such hybrid pipelines can be used to seamlessly integrate existing C-based simulation codes with Python-based machine learning frameworks.
  arXiv  Detail & Related papers  (2025-09-06T18:24:33Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems [52.19558333652367]
  We present finite-range embeddings (FiRE) for accurate large-scale ab-initio electronic structure calculations.<n>FiRE reduces the complexity of neural-network variational Monte Carlo (NN-VMC) by $sim ntextel$, the number of electrons.<n>We validate our method's accuracy on various challenging systems, including biochemical compounds and organometallic compounds.
  arXiv  Detail & Related papers  (2025-04-08T14:28:54Z)
• Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials [0.602276990341246]
  We use FieldSchNet to replace QM/MM electrostatic embedding with its ML/MM counterpart for nonadiabatic excited state trajectories.<n>Our results demonstrate that the ML/MM model reproduces the electronic kinetics and structural rearrangements of QM/MM surface hopping reference simulations.
  arXiv  Detail & Related papers  (2025-01-28T14:14:43Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactors [0.0]
  We train both physics-based and neural network models on data from the Alcator C-Mod fusion reactor. We find that a model that combines physics-based equations with a neural ODE performs better than both existing physics-motivated ODEs and a pure neural ODE model.
  arXiv  Detail & Related papers  (2023-10-30T23:25:54Z)
• 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)
• Data-driven modeling of Landau damping by physics-informed neural networks [4.728411962159049]
  We construct a multi-moment fluid model with an implicit fluid closure included in the neural network using machine learning. The model reproduces the time evolution of the electric field energy, including its damping rate, and the plasma dynamics from the kinetic simulations. This work sheds light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.
  arXiv  Detail & Related papers  (2022-11-02T10:33:38Z)
• Unsupervised Discovery of Inertial-Fusion Plasma Physics using Differentiable Kinetic Simulations and a Maximum Entropy Loss Function [77.34726150561087]
  We create a differentiable solver for the plasma kinetics 3D partial-differential-equation and introduce a domain-specific objective function. We apply this framework to an inertial-fusion relevant configuration and find that the optimization process exploits a novel physical effect.
  arXiv  Detail & Related papers  (2022-06-03T15:27:33Z)
• Hybridized Methods for Quantum Simulation in the Interaction Picture [69.02115180674885]
  We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations. Physical applications of these hybridized methods yield a gate complexity scaling as $log2 Lambda$ in the electric cutoff. For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter $lambda$ used to impose an energy cost.
  arXiv  Detail & Related papers  (2021-09-07T20:01:22Z)

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.