相关论文: PHYFU: Fuzzing Modern Physics Simulation Engines

This paper introduces an improved Electronic Stability Program for cars that can deal with the sudden burst of a tyre. The Improved Electronic Stability Program (IESP) is based on a fuzzy logic algorithm. The IESP collects data from the…

Existing methods of multi-person video 3D human Pose and Shape Estimation (PSE) typically adopt a two-stage strategy, which first detects human instances in each frame and then performs single-person PSE with temporal model. However, the…

This paper presents the design and refinement of automated Moodle-based Problem-Solving Assessments (PSAs) deployed across large-scale computing units. Developed to replace traditional exams, PSAs assess applied problem-solving skills…

Hardware security vulnerabilities in computing systems compromise the security defenses of not only the hardware but also the software running on it. Recent research has shown that hardware fuzzing is a promising technique to efficiently…

Interactive world models that simulate object dynamics are crucial for robotics, VR, and AR. However, it remains a significant challenge to learn physics-consistent dynamics models from limited real-world video data, especially for…

Execution-based evaluation of LLM-generated code implicitly treats successful execution as a proxy for correctness. In scientific simulation, this proxy is insufficient: a generated input file can run, mesh, and converge while encoding…

The precise simulation of turbulent flows is of immense importance in a variety of scientific and engineering fields, including climate science, freshwater science, and the development of energy-efficient manufacturing processes. Within the…

The increasing deployment of small Uncrewed Aerial Systems (sUAS) in diverse and often safety-critical environments demands rigorous validation of onboard decision logic under various conditions. In this paper, we present SaFUZZ, a…

This position paper takes a broad look at Physics-Enhanced Machine Learning (PEML) -- also known as Scientific Machine Learning -- with particular focus to those PEML strategies developed to tackle dynamical systems' challenges. The need to…

Satisfactory software performance is essential for the adoption and the success of a product. In organizations that follow traditional software development models (e.g., waterfall), Software Performance Engineering (SPE) involves…

Fuzzing is a popular dynamic program analysis technique used to find vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input designed to cause crashes, buffer overflows, memory errors,…

Simulation of the dynamics of physical systems is essential to the development of both science and engineering. Recently there is an increasing interest in learning to simulate the dynamics of physical systems using neural networks.…

Software testing helps developers to identify bugs. However, awareness of bugs is only the first step. Finding and correcting the faulty program components is equally hard and essential for high-quality software. Fault localization…

Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-world and scientific problems, systems that generate data are…

Cyber-Physical Systems (CPS) produce behavior through execution on substrates coupling computation with physical processes. However, usual engineering approaches do not treat execution semantics as first-class engineering entities. Formal…

The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their…

Software failures can have catastrophic and costly consequences. Functional Failure Mode and Effects Analysis (FMEA) is a standard technique used within Cyber-Physical Systems (CPS) to identify software failures and assess their…

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows.…

We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike existing datasets limited to merely hundreds or thousands of examples, PhysInOne provides 2…

ExaHyPE ("An Exascale Hyperbolic PDE Engine") is a software engine for solving systems of first-order hyperbolic partial differential equations (PDEs). Hyperbolic PDEs are typically derived from the conservation laws of physics and are…