Researchers propose training attention graph neural networks (GNNs) as neural surrogates for 3D coupled thermo-fluid fields by minimizing finite-volume method (FVM) residuals directly on the mesh, eliminating the need for labeled CFD simulation data. The approach is evaluated across four scenarios including steady-state and parametric transient cases, achieving 2.3–2.8% normalized RMSE on steady benchmarks and outperforming a supervised baseline on transient cases. The method reduces model development cost by avoiding expensive data generation from conventional numerical solvers.
COGENT is a new architecture combining graph neural networks with Neural Ordinary Differential Equations for continuous-time physical forecasting on irregular geospatial meshes. The model encodes historical system states and forcings into latent dynamics that can be queried at arbitrary future times, avoiding the error accumulation of autoregressive rollout. Evaluated on ice-sheet simulations from the Ice-sheet and Sea-level System Model, COGENT shows improved long-range stability over autoregressive graph baselines. The work introduces training stabilization strategies including rollout-horizon sampling and progressive scheduling.
A new arXiv preprint introduces a physics-informed Fourier-wavelet transformer for next-step velocity-field reconstruction in computational fluid dynamics, combining hybrid spectral encoding with PDE-residual-guided self-attention and self-supervised pretraining. The model is evaluated on cylinder-wake and fluid-structure interaction benchmarks, achieving best-in-class normalized mean-squared error on both tasks and stronger recovery of localized flow structures compared to spectral, transformer, and physics-informed neural network baselines. The work targets the persistent gap between global flow pattern accuracy and fine-grained multiscale structure recovery in surrogate models.
A new arXiv preprint introduces Error-Conditioned Neural Solvers (ENS), a method for neural PDE surrogates that passes the PDE residual field directly as input to the network at each iteration, enabling iterative self-correction rather than gradient-based residual minimization. The authors demonstrate theoretically and empirically that residual minimization is an unreliable proxy for reconstruction accuracy in ill-conditioned systems, explaining failures of prior hybrid methods. ENS achieves up to 10× accuracy gains on turbulent Kolmogorov flow across four PDE families, with lower compute cost than hybrid approaches and generalization under distribution shift including zero-shot cross-equation transfer.
A new arXiv preprint introduces Review Residuals, a mechanism that replaces fixed-coefficient residual connections in transformers with a learned, input-dependent gate conditioned on both the current hidden state and the proposed sublayer update. Experiments across five model sizes (60M–1B parameters) find no advantage at small scale but statistically significant improvements over standard residuals and Highway-style gates at 590M and 1B parameters, with the benefit growing with scale. A key finding is that convex (Highway-style) gating reintroduces vanishing gradients beyond ~20 layers, while the additive identity-preserving form trains stably at all tested depths.
Researchers introduce Controllable Neural Variational Agents (CNeVA), a framework for traffic simulation that infers per-agent Gaussian behavior latents from discounted returns via conjugate variational updates, conditioning a rectified-flow trajectory generator with classifier-free guidance. The system enables interpretable steering along axes such as speed, acceleration, and safety compliance without sacrificing realism. Evaluated on the Waymo Open Motion Dataset, CNeVA achieves competitive realism while offering per-channel controllability absent from higher-ranked imitation baselines. The work also introduces soft eligibility gates to address reward signal scarcity near decision thresholds.
Researchers introduce PromptGNN-sim, a bidirectional structure-semantic fusion framework that jointly trains a Graph Attention Network and an LLM for text-attributed graph learning. The system uses GAT-based neighborhood selection to generate structure-aware prompts for the LLM, with cross-modal contrastive learning and cross-attention aligning both components during training. Evaluated on six datasets including Cora, Pubmed, and WikiCS, it outperforms classical GNNs, standalone LLMs, and prior GNN-LLM fusion methods on cross-task transfer, cross-dataset generalization, and sparse perturbation settings.
A new arXiv preprint investigates whether intrinsic symmetries in training data produce conserved quantities during gradient-flow training of neural networks. The authors prove that for analytic, non-polynomial loss functions, data symmetries generically do not induce additional integrals of motion, but for MSE loss, data augmentation can yield extra conserved quantities. They introduce a framework of 'tensorizable networks'—architectures including linear, polynomial, and Lightning Attention networks—where parameter and input dependence can be separated via an intermediate representation.
IVGT is a new neural architecture that implicitly models continuous 3D geometry from unposed multi-view images without requiring explicit pointmap regression. It learns a continuous neural scene representation in a canonical coordinate system, supporting SDF-based surface queries and color prediction via lightweight decoders. The model is trained with multi-dataset joint optimization using 2D supervision and 3D geometric regularization, achieving strong generalization across mesh reconstruction, novel view synthesis, depth/normal estimation, and camera pose estimation tasks.