| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Artificial Intelligence Applications in Integrated Computational Materials Engineering
|
| Presentation Title |
A Bayesian Approach for Constitutive Model Selection and Calibration Using Diverse Material Responses |
| Author(s) |
Bekassyl Battalgazy, Danial Khatamsaz, Zahra Ghasemi, Raymundo Arroyave, Ankit Srivastava |
| On-Site Speaker (Planned) |
Ankit Srivastava |
| Abstract Scope |
Constitutive models in materials science and engineering provide a mathematical framework for describing and predicting the mechanical responses of materials while adhering to fundamental principles and laws. However, numerous models can satisfy fundamental principles and laws as well as describe a targeted mechanical response of a material. Thus, the selection of the most appropriate model for a material calls for the consideration of a diverse set of mechanical responses of the material under a variety of loading conditions. Herein, we propose a robust Bayesian-based workflow that integrates model selection and calibration into a single-step process while concurrently evaluating multiple reference mechanical responses of the material. To demonstrate the effectiveness of our methodology, we focus on selecting and calibrating the most suitable constitutive model capable of accurately predicting both the indentation response over a wide range of indentation strain rates and uniaxial tensile response of the material. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, ICME, Mechanical Properties |