{"total":12,"items":[{"citing_arxiv_id":"2606.18499","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Solution of the Newtonian plane Couette flow with dynamic wall slip using machine-learning methods","primary_cat":"physics.flu-dyn","submitted_at":"2026-06-16T21:17:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"PINNs and DeepONets solve Newtonian plane Couette flow with dynamic wall slip; DeepONet achieves 0.36% mean relative error on unseen cases and 540X speedup over numerical methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00296","ref_index":103,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Is Zero-Shot Super-Resolution Possible in Operator Learning?","primary_cat":"stat.ML","submitted_at":"2026-05-29T19:26:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Zero-shot super-resolution is information-theoretically impossible for some simple operators but possible under Hölder smoothness of outputs, accompanied by generalization bounds.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16966","ref_index":223,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects","primary_cat":"cs.AI","submitted_at":"2026-05-16T12:41:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A survey organizing AI methods for inverse PDE problems into inverse problems, inverse design, and control categories, covering applications and future challenges like physics-informed models and uncertainty quantification.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[34]L4DC2023MLP Adaptive PDE control MS-DeepONet [62]OJCSYS2025MLP Nonlinear predictive control 5.2 Offline-Trained Control 5.2.1 Operator learning OCPs [74]AAAI2025MLP ODE / OCP control Wanget al.[222]Arxiv2021MLP Poisson/Heat control Bhanet al.[33]IEEE Trans. Autom. ControlMLP PDE boundary control Krsticet al.[122]Automatica2024MLP Reaction-diffusion control Wanget al.[223]ACC2025MLP Hyperbolic control Qiet al.[184]SCL2024MLP Neural PDE feedback control Zhanget al.[260]Automatica2026MLP Hyperbolic control Lamarqueet al.[127]IEEE TAC 2025MLP Transport PDE control 5.2.2 Imitation learning Neural-HJB [218]Found. Data Sci.2025MLP Advection-diffusion control BEAR [171]ACM e-Energy2025MLP HVAC control 5.2.3 Generative policy learning"},{"citing_arxiv_id":"2605.08935","ref_index":147,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting","primary_cat":"cs.AI","submitted_at":"2026-05-09T13:12:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"PnP-Corrector decouples pre-trained physics engines from a correction agent to mitigate reciprocal error amplification in coupled spatiotemporal forecasting, cutting error by 28% on a 300-day ocean-atmosphere task.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04198","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Deep Wave Network for Modeling Multi-Scale Physical Dynamics","primary_cat":"cs.LG","submitted_at":"2026-05-05T18:40:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DW-Net improves the accuracy versus computational cost Pareto front over standard U-Nets for 2D and 3D multi-scale flow benchmarks by stacking multiple waves while keeping training settings identical.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27298","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DeepPropNet: an operator learning-based predictor for thermal plasma properties","primary_cat":"physics.plasm-ph","submitted_at":"2026-04-30T01:17:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DeepPropNet predicts thermal plasma properties with relative L2 errors of 10^{-3} to 10^{-2} for SF6-N2 and C4F7N-CO2-O2 mixtures using single-property and mixture-of-experts architectures trained on high-fidelity data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.24694","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Encoding strategies for quantum enhanced fluid simulations: opportunities and challenges","primary_cat":"quant-ph","submitted_at":"2026-04-27T16:54:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Encoding strategies for quantum fluid simulations trade off compactness against practicality in state preparation, measurement, boundary conditions, and nonlinear operations, with no single approach being universally optimal.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"InAIP conference proceedings, volume 2030, page 020252. AIP Publishing LLC, 2018. [11] Safae Elmisaoui, Imad Kissami, and Jean-Michel Ghidaglia. High-performance computing to accelerate large-scale computational fluid dynamics simulations: a comprehensive study. In International Conference on Advanced Intelligent Systems for Sustainable Development, pages 352-360. Springer, 2023. [12] Haixin Wang, Yadi Cao, Zijie Huang, Yuxuan Liu, Peiyan Hu, Xiao Luo, Zezheng Song, Wanjia Zhao, Jilin Liu, Jinan Sun, et al. Recent advances on machine learning for computational fluid dynamics: A survey.arXiv preprint arXiv:2408.12171, 2024. [13] Giulio Malinverno and Javier Blasco Alberto. A review of the current state-of-the-art of quantum computing for cfd: approaches, advantages, and limitations."},{"citing_arxiv_id":"2510.14099","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics","primary_cat":"quant-ph","submitted_at":"2025-10-15T21:15:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"1017/9781009023405 . arXiv:2106.10165 [cs]. http://arxiv.org/abs/2106.10165Accessed 2025-07-24 [30] Mangini, S., Tacchino, F., Gerace, D., Bajoni, D., Macchiavello, C.: Quantum computing models for artificial neural networks. Europhysics Letters134(1), 10002 (2021) https://doi.org/10.1209/0295-5075/134/10002 . arXiv:2102.03879 [quant-ph]. Accessed 2025-08-29 [31] Wang, H., Cao, Y., Huang, Z., Liu, Y., Hu, P., Luo, X., Song, Z., Zhao, W., Liu, J., Sun, J., Zhang, S., Wei, L., Wang, Y., Wu, T., Ma, Z.-M., Sun, Y.: Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey. arXiv. arXiv:2408.12171 [cs] (2024). https://doi.org/10.48550/arXiv.2408.12171 . http://arxiv.org/abs/2408.12171 Accessed 2025-09-07"},{"citing_arxiv_id":"2509.20098","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Incomplete Data, Complete Dynamics: A Diffusion Approach","primary_cat":"cs.LG","submitted_at":"2025-09-24T13:22:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A conditional diffusion model trained on partitioned incomplete samples for physical dynamics achieves asymptotic convergence to the true generative process under mild conditions and outperforms baselines in imputation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.21573","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Spectral-inspired Operator Learning with Limited Data and Unknown Physics","primary_cat":"cs.LG","submitted_at":"2025-05-27T07:25:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SINO learns PDE operators from limited data using spectral features from frequency indices, a Pi-block for nonlinearities, and a low-pass filter, achieving 1-2 orders of magnitude better accuracy than prior methods on 2D/3D benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.20349","ref_index":107,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation","primary_cat":"physics.flu-dyn","submitted_at":"2025-05-25T23:24:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"FD-Bench supplies the first modular, reproducible benchmark and leaderboard for comparing neural PDE solvers on fluid dynamics tasks with direct numerical solver baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"75], chemical engineering [16, 15, 136], biology [130, 106, 93], and environmental science [116, 79, 3, 42, 48, 84, 133]. Traditional numerical solvers have achieved remarkable success in offering high- fidelity solutions grounded in well-established mathematical formulations and rigorous convergence guarantees. Recent advancements in machine learning have introduced numerous neural solvers for data-driven fluid simulation [107]. Data-driven approaches enable efficient prediction of complex dynamics with reduced computational cost. These methods have shown strong potential in capturing nonlinear behaviors beyond the reach of traditional solvers. Despite rapid progress, the field faces significant challenges in establishing consistent evaluation standards. 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