DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism

Advanced Engineering Informatics, Год журнала: 2023, Номер 56, С. 102007 - 102007

Опубликована: Апрель 1, 2023

Язык: Английский

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An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT)

Computers in Biology and Medicine, Год журнала: 2023, Номер 158, С. 106734 - 106734

Опубликована: Март 1, 2023

Язык: Английский

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Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity

Neural Networks, Год журнала: 2023, Номер 162, С. 472 - 489

Опубликована: Март 13, 2023

Язык: Английский

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Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications

Thin-Walled Structures, Год журнала: 2024, Номер 205, С. 112495 - 112495

Опубликована: Сен. 24, 2024

Язык: Английский

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A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 127, С. 107302 - 107302

Опубликована: Ноя. 8, 2023

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential resources management. The present study aims to enhance conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven swarm computing enhancing accuracy rainfall runoff prediction. proposed framework combines (i.e. GSM) with time series predictor (SVR) optimization-driven parameter tuning firefly algorithm (SVR-FFA). This integration uniquely captures complex interplay between meteorological variables, glacier processes, responses. Applying hybrid proved better results than standalone GSM ordinary SVR in simulating series. performance integrated metaheuristic-based (W-SG-SVR-FFA) demonstrated several enhancements over model. During calibration (validation) period, evaluation metric coefficient determination (R2) was 0.77 (0.77) 0.98 (0.91) W-SG-SVR-FFA Kling-Gupta Efficiency (KGE) values were 0.81 0.97 (0.87), respectively. method glacierized catchments underscores its importance areas undergoing swift climate change glacial melting. approach enables readers witness intricate equilibrium model's complexity simulation outcomes.

Язык: Английский

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A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

npj Computational Materials, Год журнала: 2023, Номер 9(1)

Опубликована: Дек. 13, 2023

Abstract The design of materials and identification optimal processing parameters constitute a complex challenging task, necessitating efficient utilization available data. Bayesian Optimization (BO) has gained popularity in due to its ability work with minimal However, many BO-based frameworks predominantly rely on statistical information, the form input-output data, assume black-box objective functions. In practice, designers often possess knowledge underlying physical laws governing material system, rendering function not entirely black-box, as some information is partially observable. this study, we propose physics-informed BO approach that integrates physics-infused kernels effectively leverage both decision-making process. We demonstrate method significantly improves efficiency enables more data-efficient BO. applicability showcased through NiTi shape memory alloys, where are identified maximize transformation temperature.

Язык: Английский

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Incorporating dynamic recrystallization into a crystal plasticity model for high-temperature deformation of Ti-6Al-4V

Materials Science and Engineering A, Год журнала: 2023, Номер 880, С. 145211 - 145211

Опубликована: Июнь 1, 2023

Язык: Английский

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MAgNET: A graph U-Net architecture for mesh-based simulations

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108055 - 108055

Опубликована: Фев. 26, 2024

In many cutting-edge applications, high-fidelity computational models prove to be too slow for practical use and are therefore replaced by much faster surrogate models. Recently, deep learning techniques have increasingly been utilized accelerate such predictions. To enable on large-dimensional complex data, specific neural network architectures developed, including convolutional graph networks. this work, we present a novel encoder–decoder geometric framework called MAgNET, which extends the well-known networks accommodate arbitrary graph-structured data. MAgNET consists of innovative Multichannel Aggregation (MAg) layers pooling/unpooling layers, forming U-Net architecture that is analogous U-Nets. We demonstrate predictive capabilities in modeling non-linear finite element simulations mechanics solids.

Язык: Английский

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Combining crystal plasticity and phase field model for predicting texture evolution and the influence of nuclei clustering on recrystallization path kinetics in Ti-alloys

Acta Materialia, Год журнала: 2024, Номер 266, С. 119645 - 119645

Опубликована: Янв. 2, 2024

Язык: Английский

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Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108085 - 108085

Опубликована: Фев. 18, 2024

This paper proposes a novel framework for simulating the dynamics of beams on elastic foundations. Specifically, partial differential equations modeling Euler–Bernoulli and Timoshenko Winkler foundation are simulated using causal physics-informed neural network (PINN) coupled with transfer learning. Conventional PINNs encounter challenges in handling large space–time domains, even problems closed-form analytical solutions. A causality-respecting PINN loss function is employed to overcome this limitation, effectively capturing underlying physics. However, it observed that lacks generalizability. We propose solutions similar instead training from scratch by employing learning while adhering causality accelerate convergence ensure accurate results across diverse scenarios. The primary contribution lies introducing context structural engineering coupling enhance generalizability Numerical experiments beam highlight efficacy proposed approach various initial conditions, including those noise data. Furthermore, potential method demonstrated an extended spatial temporal domain. Several comparisons suggest accurately captures inherent dynamics, outperforming state-of-the-art methods under standard L2-norm metric accelerating convergence.

Язык: Английский

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