{"total":11,"items":[{"citing_arxiv_id":"2605.20028","ref_index":11,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Training-Free Bayesian Filtering with Generative Emulators","primary_cat":"cs.LG","submitted_at":"2026-05-19T15:52:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Diffusion-based generative emulators enable training-free optimal particle filtering that scales Bayesian state estimation to high-dimensional nonlinear chaotic systems including atmospheric dynamics.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15418","ref_index":78,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A General Differentiable Ray-Wave Framework for Hybrid Refractive-Diffractive System Modeling and Optimization","primary_cat":"physics.optics","submitted_at":"2026-05-14T21:04:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A plug-and-play differentiable model bridging ray and wave optics for hybrid systems that enables end-to-end optimization of planar and conformal diffractive elements.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13378","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Robust Matrix-Free Newton-Krylov Solvers via Automatic Differentiation","primary_cat":"cs.CE","submitted_at":"2026-05-13T11:34:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Forward-mode automatic differentiation replaces finite-difference approximations for Jacobian-vector products in JFNK solvers, delivering 2-3 orders of magnitude speedup and lifting minimum solver completion from 42% to 95% across Burgers, radiation diffusion, reaction-diffusion, and nonlinear time-","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07828","ref_index":36,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces","primary_cat":"math.NA","submitted_at":"2026-05-08T14:56:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"based methods require linear preconditioners, and inference employing neural networks is generally not linear. However, flexible Krylov-based methods [37] can be used with nonlinear preconditioners, so we introduce here hybrid preconditioners for relaxation methods using DeepONet. A standard relaxation method can be formulated as a preconditionedRichardsoniteration[36].The𝑖−thiteration𝐮 (𝑖) ofthesolutiongivenbyastandardrelaxationmethod is then { 𝐫(𝑖) =𝐟−𝐊𝐮 (𝑖), 𝐮(𝑖+1) =𝐮 (𝑖) + −1𝐫(𝑖), where −1denotesthespecificpreconditioneroftheRichardsoniteration,e.g.choosing −1asthediagonalorlower triangularpartof𝐊leads,respectively,totheJacobiorGauss-Seidelmethods.ThehybridpreconditionedRichardson Francesc Levrero-Florencio et al."},{"citing_arxiv_id":"2605.05421","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Policies for the Operation of an Ambulance Fleet under Uncertainty based on a New Preparedness Metric","primary_cat":"math.OC","submitted_at":"2026-05-06T20:28:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A new preparedness metric for ambulance fleet operations under uncertainty enables optimized selection and reassignment decisions and outperforms nine existing methods on real emergency medical service data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21431","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"JAX-BEM: Gradient-Based Acoustic Shape Optimisation via a Differentiable Boundary Element Method","primary_cat":"cs.CE","submitted_at":"2026-04-23T08:48:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A JAX-based differentiable BEM solver matches traditional BEM accuracy on benchmarks and supports gradient-driven acoustic geometry optimization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.21597","ref_index":34,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Low-Rank Solvers for Energy-Conserving Hamiltonian Boundary Value Methods","primary_cat":"math.NA","submitted_at":"2025-11-26T17:13:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Low-rank structure in HBVM stage equations is exploited via Krylov projection for linear cases and Newton-Krylov with adaptive time-stepping for nonlinear cases, shown efficient on semi-discretized wave equations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.09211","ref_index":113,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation","primary_cat":"math.NA","submitted_at":"2025-06-10T20:02:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"This is an introductory review of the linear algebraic subproblems and contemporary solvers in variational data assimilation for geophysical applications.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"symmetric linear systems [103], in theory only a small number of vectors of length the size of the linear system need to be stored. However, in finite precision arithmetic, there can be a loss of orthogonality that can adversely affect the rate of convergence. It may therefore be advantageous to keep (some of) the previously computed vectors and employ reorthogonalization [33, §7.5]. Other popular iterative methods, including GMRES [113], have no short-term recurrence and the number of vectors that must be held and the computational costs increase with the iteration count. In this case, it may be necessary to include strategies (such as restarting) to limit the work and storage needed. An advantage of iterative solvers is that the user can choose how many iterations to perform or specify the required accuracy in the computed solution."},{"citing_arxiv_id":"2502.09165","ref_index":38,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Generalizing Reduced Rank Extrapolation to Low-Rank Matrix Sequences","primary_cat":"math.NA","submitted_at":"2025-02-13T10:48:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Two generalizations of reduced rank extrapolation are derived for low-rank matrix sequences and iteration-dependent mapping functions, with numerical tests on Lyapunov and Riccati equations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2402.13103","ref_index":19,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data","primary_cat":"cs.LG","submitted_at":"2024-02-20T15:58:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MUDRA extends FLDA to multivariate time series with missing data via an ECM algorithm and shows improved classification over prior methods on the Articulary Word Recognition dataset.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2309.02228","ref_index":29,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Algebraic Temporal Blocking for Sparse Iterative Solvers on Multi-Core CPUs","primary_cat":"math.NA","submitted_at":"2023-09-05T13:32:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Integrating RACE into Trilinos applies algebraic temporal blocking to MPK in s-step GMRES, polynomial preconditioners, and AMG, yielding up to 3x speedups on multi-core CPUs for MPK-dominated algorithms.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}