Physics-Informed Neural Networks (PINNs) for Multi-Physics Modeling of Additively Manufactured Functionally Graded Materials

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Created by Benjamin Abolaji

Apr 3, 2026 12:12 PM

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Project Details

8 items

Department

Mechanical Engineering

Research Field

Mechanical Engineering

Advanced Manufacturing and AI

Keywords

AI Machine Learning Predictive Modeling Optimization

Collaboration Type

academic

Desired Skills

Python, MATLAB, CFD, Data Analysis, Machine Learning

Duration

Medium-term (6-24 months)

Funding

Unfunded

Target Outcome

Journal Publication

Project Description 25 words

This project leverages PINNs to model heat transfer, elasticity, and material heterogeneity in additively manufactured functionally graded materials for accurate mechanical and thermal performance prediction.

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Physics-Informed Neural Networks (PINNs) for Multi-Physics Modeling of Additively Manufactured Functionally Graded Materials

Project Details

Open Access Research Papers Free PDFs

Open Access Papers: (2023-2026)

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Physics-Informed Neural Networks (PINNs) for Multi-Physics Modeling of Additively Manufactured Functionally Graded Materials

Project Details

Open Access Research Papers Free PDFs

Open Access Papers: (2023-2026)

Team Members (1)

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Task Details