PI-JEPA: Label-Free Surrogate Pretraining for Coupled Multiphysics Simulation via Operator-Split Latent Prediction

• URL: http://arxiv.org/abs/2604.01349v3
• Date: Thu, 09 Apr 2026 02:00:04 GMT
• Title: PI-JEPA: Label-Free Surrogate Pretraining for Coupled Multiphysics Simulation via Operator-Split Latent Prediction
• Abstract summary: We introduce textbfPI-JEPA (Physics-Informed Joint Embedding Predictive Architecture), a surrogate pretraining framework that trains emphwithout any completed PDE solves<n>On single-phase Darcy flow, PI-JEPA achieves $1.9times$ lower error than FNO and $2.4times$ lower error than DeepONet at $N_ell=100$, with 24% improvement over supervised-only training at $N_ell=500$.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reservoir simulation workflows face a fundamental data asymmetry: input parameter fields (geostatistical permeability realizations, porosity distributions) are free to generate in arbitrary quantities, yet existing neural operator surrogates require large corpora of expensive labeled simulation trajectories and cannot exploit this unlabeled structure. We introduce \textbf{PI-JEPA} (Physics-Informed Joint Embedding Predictive Architecture), a surrogate pretraining framework that trains \emph{without any completed PDE solves}, using masked latent prediction on unlabeled parameter fields under per-sub-operator PDE residual regularization. The predictor bank is structurally aligned with the Lie--Trotter operator-splitting decomposition of the governing equations, dedicating a separate physics-constrained latent module to each sub-process (pressure, saturation transport, reaction), enabling fine-tuning with as few as 100 labeled simulation runs. On single-phase Darcy flow, PI-JEPA achieves $1.9\times$ lower error than FNO and $2.4\times$ lower error than DeepONet at $N_\ell{=}100$, with 24\% improvement over supervised-only training at $N_\ell{=}500$, demonstrating that label-free surrogate pretraining substantially reduces the simulation budget required for multiphysics surrogate deployment.

Related papers

• Equation Recast for Canonical Operator Learning Across Parametric PDEs [59.44573033543284]
  We introduce equation recast, which reformulates parametric operator learning as the learning of a single canonical operator.<n>In high-fidelity tokamak simulations for nuclear fusion, the framework unifies electron-temperature data across four device geometries.
  arXiv  Detail & Related papers  (2026-09-02T13:15:50Z)
• Physics-Informed Neural Operator for Warm-Starting Background-Decomposed and Preconditioned PSFD: Enabling Scalable 3-D EUV Mask Simulation [0.8747606955991706]
  We present a physics-informed neural operator (PINO) trained with pseudo-spectral frequency-domain equations for electromagnetic scattering problems in EUV lithography.<n>The PINO is trained on approximately 16,000 mask designs from the LithoBench library sampled randomly at each training iteration without using precomputed EM field solutions.<n>The PINO surrogate model yields predictions with a mean absolute error of about $7 times 10-3$ for the scattered intensity of held-out mask patterns relative to the reference PSFD solution.
  arXiv  Detail & Related papers  (2026-07-28T06:31:21Z)
• Metric--Phase Fields: Decoupling Distance and Sign for Thin-Structure Reconstruction from Unoriented Point Clouds [67.30236447078187]
  We introduce Metric-Phase Fields (MPFs), a decoupled implicit representation that separates metric proximity from topological phase.<n>Experiments on both synthetic and scanned thin-shell and thin-plate shapes demonstrate that MPFs preserve thin and layered structures more faithfully than recent SDF-based methods.
  arXiv  Detail & Related papers  (2026-05-25T07:08:57Z)
• WINO: A Weak-Form Physics Informed Neural Operator for Hyperelasticity on Variable Domains [3.8397427914728426]
  WINO is a data-free framework that combines the efficiency of neural operators with the geometric flexibility of the $varphi$-FEM method.<n> WINO achieves high accuracy below 0.04 across all benchmarks, while reducing total computational time by 50--80% compared with purely data-driven methods.
  arXiv  Detail & Related papers  (2026-05-23T16:35:08Z)
• A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation [0.0]
  Severe accident analysis using system-level codes such as MELCOR is indispensable for nuclear safety assessment.<n>Existing surrogate models accelerate these analyses but depend on large volumes of simulation data.<n> physics-informed neural networks (PINNs) enable data-free training but must be retrained for every change in problem parameters.
  arXiv  Detail & Related papers  (2026-04-03T02:50:58Z)
• Operator Learning Using Weak Supervision from Walk-on-Spheres [81.26322147849918]
  Training neural PDE solvers is often bottlenecked by expensive data generation or unstable physics-informed neural network (PINN)<n>We propose an alternative approach using Monte Carlo approaches to estimate the solution to the PDE as a process for weak supervision during training.
  arXiv  Detail & Related papers  (2026-03-01T17:23:39Z)
• Variational Entropic Optimal Transport [67.76725267984578]
  We propose Variational Entropic Optimal Transport (VarEOT) for domain translation problems.<n>VarEOT is based on an exact variational reformulation of the log-partition $log mathbbE[exp(cdot)$ as a tractable generalization over an auxiliary positive normalizer.<n> Experiments on synthetic data and unpaired image-to-image translation demonstrate competitive or improved translation quality.
  arXiv  Detail & Related papers  (2026-02-02T15:48:44Z)
• Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum [62.691095807959215]
  We establish an optimal sample complexity of $O(-2)$ for obtaining an $$-optimal global policy using a single-timescale actor-critic (AC) algorithm.<n>These mechanisms are compatible with existing deep learning architectures and require only minor modifications, without compromising practical applicability.
  arXiv  Detail & Related papers  (2026-02-02T00:35:42Z)
• Accelerating PDE Solvers with Equation-Recast Neural Operator Preconditioning [9.178290601589365]
  Minimal-Data Parametric Neural Operator Preconditioning (MD-PNOP) is a new paradigm for accelerating parametric PDE solvers.<n>It recasts the residual from parameter deviation as additional source term, where trained neural operators can be used to refine the solution in an offline fashion.<n>It consistently achieves 50% reduction in computational time while maintaining full order fidelity for fixed-source, single-group eigenvalue, and multigroup coupled eigenvalue problems.
  arXiv  Detail & Related papers  (2025-09-01T12:14:58Z)
• Optimization and generalization analysis for two-layer physics-informed neural networks without over-parametrization [0.6215404942415159]
  This work focuses on the behavior of gradient descent (SGD) in solving least-squares regression with physics-informed neural networks (PINNs)<n>We show that if the network width exceeds a threshold that depends only on $epsilon$ and the problem, then the training loss and expected loss will decrease below $O(epsilon)$.
  arXiv  Detail & Related papers  (2025-07-22T09:24:22Z)
• Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models [0.0]
  This work demonstrates a proof-of-principle for using uncertainty-aware architectures to construct efficient datasets for surrogate model generation.<n>This strategy was applied again to the plasma turbulent transport problem within tokamak fusion plasmas, specifically the QuaLiKiz quasilinear electrostatic gyrokinetic turbulent transport code.<n>With 45 active learning iterations, moving from a small initial training set of $102$ to a final set of $104$, the resulting models reached a $F_1$ classification performance of 0.8 and a $R2$ regression performance of 0.75 on an independent test set
  arXiv  Detail & Related papers  (2025-07-21T18:15:12Z)
• MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on Large Language Models [53.36415620647177]
  Semi-structured sparsity offers a promising solution by strategically retaining $N$ elements out of every $M$ weights.<n>Existing (N:M)-compatible approaches typically fall into two categories: rule-based layerwise greedy search, which suffers from considerable errors, and gradient-driven learning, which incurs prohibitive training costs.<n>We propose a novel linear-space probabilistic framework named MaskPro, which aims to learn a prior categorical distribution for every $M$ consecutive weights and subsequently leverages this distribution to generate the (N:M)-sparsity throughout an $N$-way sampling
  arXiv  Detail & Related papers  (2025-06-15T15:02:59Z)
• Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum [78.27945336558987]
  Decentralized server (DFL) eliminates reliance on client-client architecture.<n>Non-smooth regularization is often incorporated into machine learning tasks.<n>We propose a novel novel DNCFL algorithm to solve these problems.
  arXiv  Detail & Related papers  (2025-04-17T08:32:25Z)
• Coarse Graining with Neural Operators for Simulating Chaotic Systems [78.64101336150419]
  Predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling.<n>An alternative approach to such a full-resolved simulation is using a coarse grid and then correcting its errors through a temporalittext model.<n>We propose an alternative end-to-end learning approach using a physics-informed neural operator (PINO) that overcomes this limitation.
  arXiv  Detail & Related papers  (2024-08-09T17:05:45Z)
• Postselection-free approach to monitored quantum dynamics and entanglement phase transitions [0.0]
  Measurement-induced entanglement phase transitions in monitored quantum circuits have stimulated activity in a diverse research community.<n>We present a solution by introducing a scalable protocol in $U(1)$ symmetric circuits.
  arXiv  Detail & Related papers  (2024-06-27T09:59:58Z)

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.