Job Description
Job Description
• Develop and implement reduced-degree mechanical performance prediction models by combining FEA with machine learning techniques, leveraging historical datasets from previous glasses products to predict failure modes and structural instability.
• Design and deploy neural network surrogate models (e.g., using PyTorch) integrated directly into FEA solvers (such as Abaqus subroutines) to simulate history-dependent material behaviors and multiscale structural responses efficiently.
• Guide the FEA team for mechanical simulation, structural risk assessment, and design optimization of the display system, specifically applying iterative operator learning to improve out-of-distribution data transferability for new form factors.
• Develop advanced constitutive models for complex materials used in display systems (including metals, polymers, adhesives, and brittle materials) to accurately capture nonlinearities, creep, and fracture mechanics under dynamic loading.
• Develop and implement reduced-degree mechanical performance prediction models by combining FEA with machine learning techniques, leveraging historical datasets from previous glasses products to predict failure modes and structural instability.
• Design and deploy neural network surrogate models (e.g., using PyTorch) integrated directly into FEA solvers (such as Abaqus subroutines) to simulate history-dependent material behaviors and multiscale structural responses efficiently.
• Guide the FEA team for mechanical simulation, structural risk assessment, and design optimization of the display system, specifically applying iterative operator learning to improve out-of-distribution data transferability for new form factors.
• Develop advanced constitutive models for complex materials used in display systems (including metals, polymers, adhesives, and brittle materials) to accurately capture nonlinearities, creep, and fracture mechanics under dynamic loading.
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