Accelerating and Improving the Accuracy of Parameter Calibration in a Phenomenological Crystal Plasticity Model Through High-Volume Machine Learning Simulations

Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to link microstructural features with engineering-scale mechanical behaviour. Their practical use, however, is limited by the high computational cost of physics-based simulations...

Volledige beschrijving

Bibliografische gegevens

Hoofdauteurs:	Dayalan R. Gunasegaram, Najmeh Samadiani, Nathan G. March, Indrajeet Katti, David Howard, Mark Easton
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	MDPI AG 2026-03-01
Reeks:	Metals
Onderwerpen:	phenomenological constitutive model crystal plasticity deep neural network surrogate model model calibration materials engineering
Online toegang:	https://www.mdpi.com/2075-4701/16/3/295
Tags:	Voeg label toe Geen labels, Wees de eerste die dit record labelt!