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FlowBudget: Training-Free Sampling-Budget Estimation for Flow Matching Models

arXiv cs.LG ↗ · 2026-10-16 Cached

FlowBudget 提出一种免训练的采样预算估计方法,通过仿真自由的插值探针估计求解器相对的采样复杂度,从而在不进行 FID-vs-NFE 扫描的情况下确定流匹配模型所需的函数求值次数(NFE)。

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#numerical-methods

D-SLR: The Disjoint Row-Sparse plus Low-Rank Decomposition

arXiv cs.LG ↗ · 2026-10-09 Cached

论文提出 D-SLR(Disjoint Row-Sparse plus Low-Rank)分解,作为截断 SVD 的闭式替代方案,通过要求每行要么原样存储、要么由低秩拟合来消除耦合,在平方误差下不损失最优性,且无需迭代求解或调参。

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#numerical-methods

Monte-Carlo simulations

Eli Bendersky ↗ · 2026-10-08 Cached

Eli Bendersky's blog post explains Monte Carlo simulations through simple, hands-on examples, including estimating pi by dart-throwing and simulating the card game SET to answer combinatorial probability questions.

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#numerical-methods

LinearSolveBench: new benchmark for linear solvers [P]

Reddit r/MachineLearning ↗ · 2026-09-22

LinearSolveBench is a new benchmark for evaluating linear solvers in C, focusing on speed, accuracy, and generality for large sparse systems to promote algorithmic advances.

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#numerical-methods

Lecture notes on Physics Informed Neural Networks, Neural Operators, and their applications

arXiv cs.LG ↗ · 2026-09-17 Cached

These lecture notes introduce Physics Informed Neural Networks (PINNs) and Neural Operators, covering their implementation in PyTorch and open-source libraries like NVIDIA PhysicsNeMo, with applications in engineering and physics.

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#numerical-methods

Measuring Gauss-Seidel loop-carried dependency and fixing it via loop unrolling

Hacker News Top ↗ · 2026-09-15 Cached

The article investigates why the Gauss-Seidel method, which converges faster theoretically, performs slower than Jacobi due to loop-carried dependencies, and shows how loop unrolling can improve performance.

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#numerical-methods

Physics-Informed Error Field Learning: A Post-Training Optimization Framework for Physics-Informed Neural Networks

arXiv cs.LG ↗ · 2026-08-27 Cached

This paper proposes a Physics-Informed Error Field Learning (PIEFL) framework for Physics-Informed Neural Networks, introducing an auxiliary error network to improve solution accuracy in solving partial differential equations under computational constraints.

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Neural Networks with Local Converging Inputs for Efficient Options Pricing Models

arXiv cs.LG ↗ · 2026-08-05 Cached

This paper introduces Neural Networks with Local Converging Inputs (NNLCI) to accelerate PDE-based option pricing, combining coarse and refined mesh solutions to reduce RMSE by 4–12x in multi-asset Black–Scholes and Heston models.

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Walk on Decomposed Subdomains

Hacker News Top ↗ · 2026-07-28 Cached

A technical blog post introducing Walk on Decomposed Subdomains (WODS), a grid-free Monte Carlo method for solving elliptic PDEs with complex geometries, contrasted with finite differences and finite elements.

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#numerical-methods

AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies

arXiv cs.AI ↗ · 2026-06-10 Cached

AutoPDE is a code agent that explicitly represents solver strategies for partial differential equations, improving pass rate by 14.2% over baselines on the PDE Agent Bench.

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Approximating Hyperbolic Tangent

Hacker News Top ↗ · 2026-04-22 Cached

Blog post surveys fast hyperbolic tangent approximations—Taylor, Padé, splines, and bit-level tricks—for neural-network and real-time audio use.

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