{"total":13,"items":[{"citing_arxiv_id":"2605.31559","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Functional Attention: From Pairwise Affinities to Functional Correspondences","primary_cat":"cs.LG","submitted_at":"2026-05-29T17:22:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Functional Attention replaces pairwise softmax attention with structured linear operators inspired by geometric functional maps to produce compact, resolution-invariant representations for operator learning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31013","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Physics-Informed Coarsening for Multigrid Graph Neural Surrogates","primary_cat":"cs.LG","submitted_at":"2026-05-29T08:47:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proposes residual-based physics-informed coarsening in multigrid GNNs to allocate capacity to high-activity regions for more stable solid mechanics surrogates.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.28368","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LEIA: Learned Environment for Interactive Architected Materials","primary_cat":"cs.LG","submitted_at":"2026-05-27T12:04:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27968","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning","primary_cat":"cs.CE","submitted_at":"2026-05-27T05:03:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LoRA adapters enable a 61.47M-parameter aerodynamics Transformer pretrained on four vehicle families to adapt to a held-out fifth family with 20 samples, reaching R²=0.85 and outperforming full fine-tuning and from-scratch training with 3x more data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22182","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"IKNO: Infinite-order Kernel Neural Operators","primary_cat":"cs.LG","submitted_at":"2026-05-21T08:52:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"IKNO replaces first-order kernel integrals in neural operators with infinite-order versions that have efficient closed-form approximations and reports SOTA accuracy on time-dependent and time-independent benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15231","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation","primary_cat":"cs.LG","submitted_at":"2026-05-13T18:04:58+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12343","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scale","primary_cat":"cs.LG","submitted_at":"2026-05-12T16:20:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Local neural operators on 3x3x3 patches, composed via Schwarz iteration, solve large-scale nonlinear elasticity on arbitrary geometries without domain-specific retraining.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"operators extend this idea to operator learning on irregular domains, using graph kernels, point-cloud representations, signed-distance functions, or learned mappings between irregular and regular domains [18]. More recently, transformer architectures have advanced PDE modeling; Transolver [19, 20] handles complex geometries via physics-aware attention mechanisms [21], whereas HAMLET [22] tackles parametric problems using graph attention. These approaches are important steps toward geometry-aware and parametric learned simulation. However, they still primarily follow a global learning paradigm: the model is trained to map from a full geometry and its associated physical inputs to a full solution field. Thus, generalization to"},{"citing_arxiv_id":"2605.11111","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ShardTensor: Domain Parallelism for Scientific Machine Learning","primary_cat":"cs.DC","submitted_at":"2026-05-11T18:20:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"horovod[44], and now most commonly through PyTorch's DDP[45]. Data parallel learning, as the name implies, allows parallelizing over the batch dimension to arbitrary scale (pro- vided the computational resource allows it, and the dataset size is large enough). With data parallel learning came significant research into strong-scaling machine learning algorithms, with focus on optimizers [46], [47] to accelerate convergence and set record training times for challenging problems [48]. As model parameter counts grew in the early 2020s, the era of Large Language Models led to new developments in model parallelization. Some of the earliest billion parameter models via the Megatron [49] framework from NVIDIA led to break- throughs in convergence of language models."},{"citing_arxiv_id":"2605.00062","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics","primary_cat":"eess.IV","submitted_at":"2026-04-30T06:43:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"RETO achieves relative L2 errors of 0.063 on ShapeNet and 0.089/0.097 on DrivAerML surface pressure/velocity, outperforming Transolver and other baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25985","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning Neural Operator Surrogates for the Black Hole Accretion Code","primary_cat":"astro-ph.HE","submitted_at":"2026-04-28T17:08:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Physics-informed Fourier neural operators recover plasmoid formation in sparse SRRMHD vortex data where data-only models fail, and transformer operators approximate AMR jet evolution, marking first reported uses in these relativistic MHD settings.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"preprint arXiv:2506.16656(2025). [42] Shizheng Wen et al. \"Geometry aware operator transformer as an efficient and accurate neural surrogate for pdes on arbitrary domains\". In:arXiv preprint arXiv:2505.18781(2025). [43] Haixu Wu et al. \"Transolver: A fast transformer solver for pdes on general geometries\". In:arXiv preprint arXiv:2402.02366(2024). [44] Huakun Luo et al. \"Transolver++: An accurate neural solver for pdes on million-scale geometries\". In: arXiv preprint arXiv:2502.02414(2025). [45] Zeyi Xu et al. \"AMR-Transformer: enabling efficient long-range interaction for complex neural fluid sim- ulation\". In:Proceedings of the Computer Vision and Pattern Recognition Conference. 2025, pp. 5804-"},{"citing_arxiv_id":"2604.18012","ref_index":48,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural Shape Operator Surrogates -- Expression Rate Bounds","primary_cat":"cs.LG","submitted_at":"2026-04-20T09:35:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Neural and spectral operators can approximate shape-to-solution maps for families of elliptic and parabolic PDEs and BIEs with provable uniform error bounds derived from parametric holomorphy on a reference domain.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.11403","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions","primary_cat":"cs.CE","submitted_at":"2026-04-13T12:44:04+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Scale-autoregressive modeling (SAR) samples fluid flow distributions hierarchically from coarse to fine resolutions on meshes, achieving lower distributional error and 2-7x faster runtime than diffusion or flow-matching baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05652","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies","primary_cat":"physics.flu-dyn","submitted_at":"2026-04-07T09:54:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DDS-PINN uses localized neural networks plus a unified global loss to model multiscale fluid flows with long-range dependencies, achieving CFD-comparable accuracy on laminar backward-facing step flow with zero data and O(10^-4) error on turbulent flow with only 500 supervision points.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}