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introduced as a diagnostic for learned simulators on 1D heat and Burgers equations; it correlates with rollout degradation (Spearman ρ=0.635) while regularization shows mixed results.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.24573","ref_index":28,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"AstroMind: A High-Fidelity Benchmark for Spacecraft Behavior Reasoning Based on Large Language Models","primary_cat":"cs.CL","submitted_at":"2026-05-23T13:23:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AstroMind is a new physics-grounded benchmark for LLM reasoning on spacecraft behavior across intent inference, maneuver estimation, and threat assessment, evaluated on several open-weight 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September 6, 2011 using the SOLERwave Tool","primary_cat":"astro-ph.SR","submitted_at":"2026-05-22T13:08:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Analysis of the September 6, 2011 coronal wave with the SOLERwave multi-sector method reveals over 40% speed variation (750-1500 km/s) between northward and northwestward segments, attributed to differences in magnetosonic speed from an MHD solution.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23510","ref_index":34,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning partially observed systems with neural Hamiltonian ordinary differential equations","primary_cat":"cs.LG","submitted_at":"2026-05-22T11:18:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"NHODE framework learns partially observed dynamical systems by combining Hamiltonian neural networks with neural ODEs, enforcing energy conservation and improving long-horizon stability over data-driven baselines on mass-spring and three-body problems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16837","ref_index":45,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning inelastic constitutive models from stress-strain data under hard thermodynamic constraints","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-16T06:40:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A thermodynamics-constrained ML framework learns robust, consistent constitutive models for inelastic materials from macroscopic stress-strain data and generalizes to unseen paths.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22845","ref_index":6,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming","primary_cat":"cs.CE","submitted_at":"2026-05-14T17:12:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CAttBiGNN is a bipartite GNN with edge-aware cross attention that predicts coupled nodal displacements and elemental thinning for autoregressive rollout of explicit dynamic FE simulations on dome and corner forming benchmarks, outperforming node-centered baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13268","ref_index":17,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Physics Guided Generative Optimization for Trotter Suzuki Decomposition","primary_cat":"quant-ph","submitted_at":"2026-05-13T09:48:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"P-GONE applies generative ML to optimize Trotter-Suzuki decompositions, reporting up to 19.4x circuit depth reduction at F >= 0.95 versus Qiskit baselines on structured Hamiltonians.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11266","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"PG-3DGS: Optimizing 3D Gaussian Splatting to Satisfy Physics Objectives","primary_cat":"cs.CV","submitted_at":"2026-05-11T21:43:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PG-3DGS couples 3D Gaussian Splatting with differentiable physics so that optimized shapes satisfy both visual fidelity and physical objectives such as pouring and aerodynamic lift, with real-world 3D-printed validation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"collocation points, alongside any available supervision such as initial and boundary conditions [20]. More broadly, PINNs can be viewed as a form of physics-informed machine learning in which physical laws act as informative priors and are enforced as soft penalty constraints through the training objective, improving data efficiency and discouraging physically implausible predictions [21]. However, enforcing physics through composite losses can lead to challenging optimization behavior: stiff gradient-flow dynamics may induce severe imbalance between the PDE residual and data/constraint terms, resulting in unstable training and inaccurate solutions unless the loss is carefully balanced or adaptively reweighted [22]. In our setting, we"},{"citing_arxiv_id":"2605.11111","ref_index":17,"ref_count":1,"confidence":0.88,"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":"First, the data in scientific models is typically ofhigh spatial resolution, with scientists working with a \"more is better\" philosophy - and rightly so. Higher resolution imaging across a breadth of scientific domains often leads to breakthrough results, from the first ever images of a black hole [16] to achieving atomic-resolution protein structures in cryo-electron microscopy [17], and mapping human cerebral cortexes at petavoxel scales [18]. In multi-decadal Earth System projection, climate-critical cloud-forming turbulent processes require tens of meters in space and seconds in time to satisfyingly resolve, which remains far beyond the computational capacity of even the most ambitious global simulation frameworks [19]-[22]."},{"citing_arxiv_id":"2605.11033","ref_index":5,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma","primary_cat":"physics.plasm-ph","submitted_at":"2026-05-10T23:38:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TokaMind, pre-trained on MAST tokamak data, transfers to power grid PMU data for severe event classification with F1 0.837, where difficulty depends on grid topology and CSD indicators boost early-warning performance over CNN baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"and risks of foundation models.arXiv preprint arXiv:2108.07258, 2021. URL https://arxiv. org/abs/2108.07258. [4] George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. Physics-informed machine learning.Nature Reviews Physics, 3(6):422-440, 2021. doi: 10.103 8 8/s42254-021-00314-5. URL https://doi.org/ 10.1038/s42254-021-00314-5. [5] Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo, Gianluigi Rozza, Maziar Raissi, and Francesco Piccialli. Scientific machine learn- ing through physics-informed neural networks: Where we are and what's next.Journal of Sci- entific Computing, 92(3):88, 2022. doi: 10.1007/ s10915-022-01939-z. [6] Shashank Subramanian, Peter Harrington, Kurt"},{"citing_arxiv_id":"2605.09523","ref_index":16,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"HS-FNO: History-Space Fourier Neural Operator for Non-Markovian Partial Differential Equations","primary_cat":"cs.LG","submitted_at":"2026-05-10T13:14:59+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":8.0,"formal_verification":"none","one_line_summary":"HS-FNO lifts the state to include history and decomposes updates into a learned future-slice predictor plus an exact shift-append transport, yielding lower rollout errors than standard or lag-stack FNO baselines on five non-Markovian PDE families.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"neural networks and physics-informed machine learning incorporate differential-equation structure into training objectives [15, 16]. For DPDEs, Huang and Zhu [17] proposed a double-activation PINN-style network with piecewise fitting for derivative discontinuities in parabolic equations with time delay, while Wang et al. [18] developed a PINN- DDE framework for forward and inverse DDE problems and delay-parameter estimation. Feng et al. [19] used Fourier neural operators for high-dimensional time-delay chaotic systems by mapping one history segment to the next. Statistics- informed neural networks provide another relevant JCP example: Zhu et al. [ 20] learned stochastic Markovian and non-Markovian dynamics by matching statistical behavior motivated by projection-operator modeling. A broader"}],"limit":50,"offset":0}