computational fluid dynamics
PulseAugur coverage of computational fluid dynamics — every cluster mentioning computational fluid dynamics across labs, papers, and developer communities, ranked by signal.
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LLM Agent Simplifies AI for Laser Powder Bed Fusion Process Optimization
Researchers have developed AgentAM, a large language model (LLM) agent designed to provide process engineers with natural-language access to predictive models for laser powder bed fusion (LPBF). This agent utilizes comp…
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FlowMesh framework advances physical field modeling on unstructured meshes
Researchers have developed FlowMesh, a novel generative framework designed for stable distributional modeling of physical fields on unstructured meshes. This approach combines permutation-invariant mesh encoding with de…
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New neural operator FG-ConvNO predicts propeller CFD with limited data
Researchers have developed FG-ConvNO, a novel neural operator designed for predicting computational fluid dynamics (CFD) in complex geometries, particularly for propeller designs. This model addresses the challenge of l…
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New data-free CNN developed for fluid dynamics simulations
Researchers have developed a new data-free finite-volume convolutional neural network (FV-CNN) designed to simulate steady compressible Euler flow around a circular cylinder. This method utilizes shared-face fluxes for …
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New physics-guided surrogate model improves cavity geometry prediction
Researchers have developed a novel physics-guided multi-fidelity surrogate framework called Extended Hierarchical Kriging (PG-EHK) to predict the natural supercavity geometry of axisymmetric conical cavitators. This app…
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Neural networks compared for indoor environment prediction
A new research paper compares two types of geometry-conditioned neural networks, Canvas-based neural networks (CaNN) and coordinate-based neural networks (CoNN), for predicting indoor airflow and temperature. The study …
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Diffusion models enhance localized ship hull design with high-fidelity assessment
Researchers have developed a novel diffusion model framework for optimizing ship hull designs, focusing on localized refinements. This approach uses parametric masking to control geometric changes in specific areas like…
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New AI Model Predicts Vehicle Aerodynamics with High Accuracy
Researchers have developed HGPTrans, a novel Hierarchical Graph-Pooling Transolver model designed to rapidly predict aerodynamic drag coefficients for vehicles. This model integrates graph isomorphism convolutions for l…
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AI framework optimizes data center thermal planning with 10000x speedup
Researchers have developed an AI-driven framework to optimize data center capacity planning, addressing the thermal challenges posed by large language models. This framework utilizes an AI model that learns from various…
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Neural fields win aerodynamic prediction challenge with ensemble learning
Researchers have developed a novel approach using neural field ensembles to predict aerodynamic surface properties, achieving first place in the ONERA CRM Wall Distribution Regression Challenge. This method models the p…
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Physics-Informed Neural Networks applied to aortic aneurysm study
Researchers have developed a novel three-dimensional Physics-Informed Neural Network (PINN) framework to study blood flow dynamics within the human aorta. This model simulates pulsatile blood flow over a two-minute peri…
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New Flow State Attention Network Enhances Aerodynamic Prediction Accuracy
Researchers have introduced the Flow State Attention Network (FSAN), a novel deep learning model designed to improve the accuracy and applicability of aerodynamic predictions. Traditional computational fluid dynamics (C…
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VFNet: New neural network estimates void fraction from multi-view videos
Researchers have developed VFNet, a novel dual-branch spatio-temporal neural network designed to estimate void fraction in gas-liquid two-phase flow using synchronized multi-view videos. This method overcomes limitation…
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New framework fuses CFD and wind-tunnel data to boost aerospace model accuracy
Researchers have developed a novel data fusion framework to improve the accuracy of aerospace surrogate models by integrating experimental wind-tunnel data with computational fluid dynamics (CFD) simulations. The framew…
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Deep learning enhances 4D flow MRI for better blood flow assessment
Researchers have developed a deep learning framework to improve the resolution and reduce noise in 4D flow MRI data, a technique used for visualizing blood flow. The proposed model integrates multi-scale feature extract…
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New VATO method enhances prediction of unsteady aerofoil flows
Researchers have developed VATO (Vortex-Force-Aware Transformer Operator), a novel approach that integrates the Vortex Force Map (VFM) method with a geometry-aware neural operator to improve the prediction of unsteady s…
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New framework models constrained multi-output physical fields using row-wise PCA and GP
Researchers have developed a novel framework for simultaneously predicting multiple high-dimensional physical fields that are subject to linear equality constraints. This problem is common in physics and machine learnin…
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New STCO framework enhances neural operator predictions for time-dependent PDEs
Researchers have introduced STCO, a novel conditional neural operator designed for time-dependent partial differential equations (PDEs). This new framework allows for predictions conditioned on prescribed inputs like bo…
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Residual Learning Improves Unsteady Aerodynamic Load Prediction
Researchers have explored the use of residual learning with long short-term memory (LSTM) neural networks to enhance the prediction of unsteady aerodynamic loads for aeroelastic applications. The study utilized the NLR …
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New Deep Learning Model Enhances AAA Hemodynamic Prediction
Researchers have developed a novel Modified Multi-Input Multi-Output Physics-Informed DeepONet (M3PI-DeepONet) architecture to more accurately predict complex 3D blood flow dynamics in Abdominal Aortic Aneurysms (AAAs).…