计算工程、金融与科学

Accurately predicting traffic accidents in real-time is a critical challenge in autonomous driving, particularly in resource-constrained environments. Existing solutions often suffer from high computational overhead or fail to adequately…

This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector…

Multiple model reduction techniques have been proposed to tackle linear and non linear problems. Intrusive model order reduction techniques exhibit high accuracy levels, however, they are rarely used as a standalone industrial tool, because…

Accurate prediction of expected concentrations is essential for effective catchment management, requiring both extensive monitoring and advanced modeling techniques. However, due to limitations in the equation solving capacity, the…

With the development of quantum computing, the use of quantum algorithms to solve combinatorial optimization problems on quantum computers has become a major research focus. The Quadratic Unconstrained Binary Optimization (QUBO) model…

Generative models have demonstrated remarkable success in domains such as text, image, and video synthesis. In this work, we explore the application of generative models to fluid dynamics, specifically for turbulence simulation, where…

The accurate modeling of the mechanical behavior of rubber-like materials under multi-axial loading constitutes a long-standing challenge in hyperelastic material modeling. This work employs deep symbolic regression as an interpretable…

The paper presents a cradle-to-gate sustainability assessment methodology specifically designed to evaluate aircraft components in a robust and systematic manner. This methodology integrates multi-criteria decision-making (MCDM) analysis…

Hydro-geomechanical models are required to predict or understand the impact of subsurface engineering applications as, for example, in gas storage in geological formations. This study puts a focus on engineered carbonate precipitation…

Machine Learning methods have extensively evolved to support industrial big data methods and their corresponding need in gas turbine maintenance and prognostics. However, most unsupervised methods need extensively labeled data to perform…

We develop robust and scalable fully implicit nonlinear finite element solvers for the simulations of biological transportation networks driven by the gradient flow minimization of a non-convex energy cost functional. Our approach employs a…

This study aims to present a new hybrid method for weighting criteria. The methodological project combines the ENTROPY and CRITIC methods with the TOPSIS method to create EC-TOPSIS. The difference lies in the use of a weight range per…

We present a thermodynamically consistent three-phase model for the coupled thermal transport and mechanical deformation of ceramic aerogel porous composite materials, which is formulated via continuum mixture theory. The composite…

Parameter estimation in structural dynamics generally involves inferring the values of physical, geometric, or even customized parameters based on first principles or expert knowledge, which is challenging for complex structural systems. In…

Periodic fin structures are often employed to enhance heat transfer in compact cooling solutions and heat exchangers. Adjoint-based optimization methods are able to further increase the heat transfer by optimizing the fin geometry. However,…

We present an adaptive Finite State Projection (FSP) method for efficiently solving the Chemical Master Equation (CME) with rigorous error control. Our approach integrates time-stepping with dynamic state-space truncation, balancing…

This paper demonstrates that deep learning models trained on raw OHLCV (open-high-low-close-volume) data can achieve comparable performance to traditional machine learning (ML) models using technical indicators for stock price prediction in…

This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most…

This study presents an advanced numerical framework that integrates experimentally acquired Atomic Force Microscope (AFM) data into high-fidelity simulations for adhesive rough contact problems, bridging the gap between experimental physics…

Model rocketry presents a design task accessible to undergraduates while remaining an interesting challenge. Allowing for variation in fins, nose cones, and body tubes presents a rich design space containing numerous ways to achieve various…