About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Verification, Calibration, and Validation Approaches for Mechanical Modeling of Metals
|
| Presentation Title |
A probabilistic framework for irreversible kinetics: unifying forward evolution, inverse inference, and uncertainty quantification |
| Author(s) |
Manas V. Upadhyay |
| On-Site Speaker (Planned) |
Manas V. Upadhyay |
| Abstract Scope |
Constitutive models are widely employed to predict mechanical response of metallic materials, yet deterministic formulations provide limited information regarding uncertainty in model predictions and reconstruction of unknown histories from sparse observations. This work presents a novel probabilistic framework for irreversible kinetics that unifies forward-in-time evolution and inverse inference within a single formulation. Admissible histories are assigned a Gibbs-type probability measure constructed from a constrained action functional that encodes constitutive physics and available observations. The resulting posterior distribution enables uncertainty quantification over entire evolution histories, while recovering classical deterministic updates in the zero-uncertainty limit. The framework is demonstrated for incremental energy–dissipation models representative of generalized standard materials and generalized gradient flows. Seven representative forward-in-time examples (viscoelasticity, rate-independent and rate-dependent plasticity, gradient damage, Allen-Cahn and Cahn-Hilliard phase field models, and coupled thermoelectric Onsager system), and an inverse inference problem in which unknown histories are reconstructed from sparse endpoint observations are presented. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Other, Other |