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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Physics Informed Neural Network Recovery of Continuous Relaxation Time Spectra in Linear Viscoelasticity
Author(s) Bright I. Simeon, Mehrdad Negahban
On-Site Speaker (Planned) Bright I. Simeon
Abstract Scope Spectral decomposition of dynamic mechanical data into a material’s continuous relaxation time spectrum is a long-standing inverse problem. All solutions obtained so far come primarily from regularization-based and analytical-based methods. As the inversion is ill-posed, the solutions fail by merging close peaks in Tikhonov’s regularization or need mode preassignment before one can do mode optimization. To address these problems, we introduce a physics informed neural network (PINN) to directly recover the continuous spectrum from storage and loss modulus data without any prior knowledge of the number of modes. The network is tested over a range of mode spacings extending down to 0.30 decades. We found a resolution boundary of approximately 0.5-0.6 decades below which the error is 5%-22%, while above this limit the error is less than 1.5%. This demonstrates that the network has a better performance than the fixed one-decade floor of the errors provided by traditional Tikhonov regularization.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Other

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