Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter

• URL: http://arxiv.org/abs/2303.14090v2
• Date: Thu, 19 Oct 2023 15:20:09 GMT
• Title: Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
• Authors: Zhenyu Dai, Ben Moews, Ricardo Vilalta, Romeel Dave
• Abstract summary: This paper presents the first application of physics-informed neural networks to baryon inpainting. We introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction.
• Score: 1.786053901581251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the Simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.

Related papers

• Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems [11.456980278006574]
  This paper presents an improved physics-driven neural network (IPDNN) framework for solving electromagnetic inverse scattering problems (ISPs)<n>A new Gaussian-localized oscillation-suppressing window (GLOW) activation function is introduced to stabilize convergence and enable a lightweight yet accurate network architecture.<n>The proposed solver achieves superior reconstruction accuracy, robustness, and efficiency compared with existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-12-10T05:41:09Z)
• From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics [70.27068115318681]
  We introduce a neural-based ''subgrid black hole'' that learns the small-scale local dynamics and embeds it within direct simulations.<n>Thanks to the great speedup in fine-scale evolution, our approach captures intrinsic variability in accretion-driven feedback, allowing dynamic coupling between the central black hole and galaxy-scale gas.
  arXiv  Detail & Related papers  (2025-12-01T11:47:49Z)
• Hybrid Physical-Neural Simulator for Fast Cosmological Hydrodynamics [2.5807659587068534]
  We propose a hybrid approach where gravitational forces are computed using a differentiable particle-mesh solver, while the hydrodynamics are parametrized by a neural network.<n>The approach is furthermore highly data efficient, with a single reference simulation of cosmological structure formation being sufficient to constrain the neural pressure model.
  arXiv  Detail & Related papers  (2025-10-30T15:18:07Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• KITINet: Kinetics Theory Inspired Network Architectures with PDE Simulation Approaches [43.872190335490515]
  This paper introduces KITINet, a novel architecture that reinterprets feature propagation through the lens of non-equilibrium particle dynamics.<n>At its core, we propose a residual module that models update as the evolution of a particle system.<n>This formulation mimics particle collisions and energy exchange, enabling adaptive feature refinement via physics-informed interactions.
  arXiv  Detail & Related papers  (2025-05-23T13:58:29Z)
• A Physics-Informed Convolutional Long Short Term Memory Statistical Model for Fluid Thermodynamics Simulations [0.0]
  Direct numerical simulations of fluid thermodynamics are computationally prohibitive.<n>We present a physics-informed architecture for RBC, a canonical example of convective flow.<n>Inference is penalized with respect to the governing partial differential equations to ensure interpretability.
  arXiv  Detail & Related papers  (2025-05-16T06:47:00Z)
• PINP: Physics-Informed Neural Predictor with latent estimation of fluid flows [11.102585080028945]
  We propose a new physics-informed learning approach that incorporates coupled physical quantities into the prediction process. By incorporating physical equations, our model demonstrates temporal extrapolation and spatial generalization capabilities.
  arXiv  Detail & Related papers  (2025-04-08T14:11:01Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter. In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• Efficient and precise quantum simulation of ultra-relativistic quark-nucleus scattering [0.35998666903987897]
  We present an efficient and precise framework to simulate the dynamics of the quark-nucleus scattering. Our framework can be generalized to simulate the dynamics of various scattering problems in quantumdynamics.
  arXiv  Detail & Related papers  (2024-03-31T22:48:05Z)
• Rethinking materials simulations: Blending direct numerical simulations with neural operators [1.6874375111244329]
  We develop a new method that blends numerical solvers with neural operators to accelerate such simulations. We demonstrate the effectiveness of this framework on simulations of microstructure evolution during physical vapor deposition.
  arXiv  Detail & Related papers  (2023-12-08T23:44:54Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition [67.46012350241969]
  This paper proposes a general acceleration methodology called NeuralStagger. It decomposing the original learning tasks into several coarser-resolution subtasks. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
  arXiv  Detail & Related papers  (2023-02-20T19:36:52Z)
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines [116.33694183176617]
  We use a neural network based on the Recurrent Inference Machine to reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions.
  arXiv  Detail & Related papers  (2023-01-10T19:00:12Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Optimising hadronic collider simulations using amplitude neural networks [0.0]
  We train neural network models on one-loop amplitudes from the NJet C++ library and interface them with the Sherpa Monte Carlo event generator. We find excellent agreement in the distributions and a reduced total simulation time by a factor of thirty.
  arXiv  Detail & Related papers  (2022-02-09T15:08:30Z)
• Predicting Dynamics of Transmon Qubit-Cavity Systems with Recurrent Neural Networks [0.0]
  Current models based on solutions to master equations are not sufficient in capturing the non-Markovian dynamics at play. We present a method of overcoming this by using a recurrent neural network to obtain effective solutions to the Lindblad master equation for a coupled transmon qubit-cavity system.
  arXiv  Detail & Related papers  (2021-09-29T15:02:23Z)
• Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling [47.081318079190595]
  We investigate the existence of underlying structures in the empirical coefficients using autoencoders. We design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients. The spiral module is evaluated on multiple neural network architectures and consistently achieves better speed-accuracy trade-off than baseline models.
  arXiv  Detail & Related papers  (2021-07-09T09:03:08Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes [1.2599533416395767]
  Baryonic properties require complex hydrodynamic simulations that are computationally costly to run. We merge an extended version of the equilibrium model, an analytic formalism describing the evolution of the stellar, gas, and metal content of galaxies, into a machine learning framework. We create a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties.
  arXiv  Detail & Related papers  (2020-12-10T16:50:33Z)

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.