Tushar Gautam | Searcharxiv

arXiv2025

SIFBench: An Extensive Benchmark for Fatigue Analysis

Fatigue-induced crack growth is a leading cause of structural failure across critical industries such as aerospace, civil engineering, automotive, and energy. Accurate prediction of stress intensity factors (SIFs) -- the key parameters governing crack propagation in linear elastic fracture mechanics -- is essential for assessing fatigue life and ensuring structural integrity. While machine learning (ML) has shown great promise in SIF prediction, its advancement has been severely limited by the lack of rich, transparent, well-organized, and high-quality datasets. To address this gap, we introduce SIFBench, an open-source, large-scale benchmark database designed to support ML-based SIF prediction. SIFBench contains over 5 million different crack and component geometries derived from high-fidelity finite element simulations across 37 distinct scenarios, and provides a unified Python interface for seamless data access and customization. We report baseline results using a range of popular ML models -- including random forests, support vector machines, feedforward neural networks, and Fourier neural operators -- alongside comprehensive evaluation metrics and template code for model training, validation, and assessment. By offering a standardized and scalable resource, SIFBench substantially lowers the entry barrier and fosters the development and application of ML methods in damage tolerance design and predictive maintenance.

cs.DB↗

arXiv2021

The Reads-From Equivalence for the TSO and PSO Memory Models

The verification of concurrent programs remains an open challenge due to the non-determinism in inter-process communication. One algorithmic problem in this challenge is the consistency verification of concurrent executions. Consistency verification under a reads-from map allows to compute the reads-from (RF) equivalence between concurrent traces, with direct applications to areas such as Stateless Model Checking (SMC). The RF equivalence was recently shown to be coarser than the standard Mazurkiewicz equivalence, leading to impressive scalability improvements for SMC under SC (sequential consistency). However, for the relaxed memory models of TSO and PSO (total/partial store order), the algorithmic problem of deciding the RF equivalence, as well as its impact on SMC, has been elusive. In this work we solve the problem of consistency verification for the TSO and PSO memory models given a reads-from map, denoted VTSO-rf and VPSO-rf, respectively. For an execution of $n$ events over $k$ threads and $d$ variables, we establish novel bounds that scale as $n^{k+1}$ for TSO and as $n^{k+1}\cdot \min(n^{k^2}, 2^{k\cdot d})$ for PSO. Based on our solution to these problems, we develop an SMC algorithm under TSO and PSO that uses the RF equivalence. The algorithm is exploration-optimal, in the sense that it is guaranteed to explore each class of the RF partitioning exactly once, and spends polynomial time per class when $k$ is bounded. We implement all our algorithms in the SMC tool Nidhugg, and perform a large number of experiments over benchmarks from existing literature. Our experimental results show that our algorithms for VTSO-rf and VPSO-rf provide significant scalability improvements over standard alternatives. When used for SMC, the RF partitioning is often much coarser than the standard Shasha-Snir partitioning for TSO/PSO, which yields a significant speedup in the model checking task.

cs.PL↗