Fabian Waschkowski
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View article: Unlocking Vision-Language Models for Video Anomaly Detection via Fine-Grained Prompting
Unlocking Vision-Language Models for Video Anomaly Detection via Fine-Grained Prompting Open
Prompting has emerged as a practical way to adapt frozen vision-language models (VLMs) for video anomaly detection (VAD). Yet, existing prompts are often overly abstract, overlooking the fine-grained human-object interactions or action sem…
View article: Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models
Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models Open
Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as Gene Expression Programming (GEP). This paper introduces the concept of adaptive symbols to the GEP framework …
View article: A coupled framework for symbolic turbulence models from deep-learning
A coupled framework for symbolic turbulence models from deep-learning Open
Improvements in turbulence modelling in the recent years has seen an increasing prominence of various machine-learning algorithms. In this work, two different algorithms: tensor basis neural networks (TBNNs) and gene-expression programming…
View article: Turbulence Model Development based on a Novel Method Combining Gene Expression Programming with an Artificial Neural Network
Turbulence Model Development based on a Novel Method Combining Gene Expression Programming with an Artificial Neural Network Open
Data-driven methods are widely used to develop physical models, but there still exist limitations that affect their performance, generalizability and robustness. By combining gene expression programming (GEP) with artificial neural network…
View article: Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models
Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models Open
Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as Gene Expression Programming (GEP). This paper introduces the concept of adaptive symbols to the GEP framework …
View article: Multi-objective CFD-driven development of coupled turbulence closure models
Multi-objective CFD-driven development of coupled turbulence closure models Open
View article: Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning
Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning Open
Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) dr…
View article: Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning
Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning Open
Existing Reynolds Averaged Navier-Stokes based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this study, a novel framework based on computational fluids dynamics driven m…