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Field
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-mechanical phase-field model incorporating hydrogen diffusion, mechanical degradation, and fracture evolution. - Employ physics-informed neural networks (PINNs) to infer hidden fields and accelerate
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Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather
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-edge solutions and pushing the boundaries in the field Develop advanced artificial neural networks (ANN), including training, mapping, and weight quantization Collaborate with cross-functional teams
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11.11.2024, Wissenschaftliches Personal In the project “BIG-ROHU” (BIG Data - Rotor Health and Usage Monitoring), a system is being developed which provides information on both the health and the actual stress of helicopter components using a data-based as well as a physics-based approach. In...
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, the internal workings of deep neural networks remain largely mysterious, posing a significant challenge to the interpretability, reliability, and further advancement of these models. This project seeks deep
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, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from nonlinear control and optimisation
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for tomagraphic imaging in tissue Neural network correction of distortions in acoustic transducers web page For further details or alternative project arrangements, please contact: alexis.bishop@monash.edu.
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temporarily, as needed, when needed. The goal of this project is to advance the understanding of how working memory is implemented in the human brain. To this end, the main objective is to develop a neural
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and