Key computational modeling issues in Integrated Computational Materials Engineering
By Jitesh H. Panchal, Surya R. Kalidindi, David L. McDowell. Comput. Aided Des., 2013
347 citations recorded by the source corpus.
Subfields: Materials Chemistry, Mechanical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/b6fca123cc1dd06ef71fcf33a3e8a28a467293bb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- Franz Roters
- Dierk Raabe
- M. Diehl
- Philip Eisenlohr
- Duancheng Ma
- Pratheek Shanthraj
- Rohit Batra
- Rampi Ramprasad
In Mechanical Engineering
- Dierk Raabe
- Franz Roters
- Philip Eisenlohr
- M. Diehl
- Duancheng Ma
- Pratheek Shanthraj
- Martin Friák
- Robert Akid
In Mechanics of Materials
- Franz Roters
- M. Diehl
- Dierk Raabe
- Philip Eisenlohr
- Kun Wang
- Duancheng Ma
- Pratheek Shanthraj
- H. Fabritius
Citations in the corpus
- Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (2017, background): “The latter is characterized by significant randomness which invariably implies gigantic numbers of random variables [36] and must be propagated across different length scales [60] in the context of simulation-based analysis and design [57, 86].”
- Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning (2018, methodology): “To overcome this computational barrier, surrogate models are often derived to replicate the homogenized responses of sub-scale simulations [Kirane and Ghosh, 2008, Verhoosel et al., 2010, Ke-shavarz and Ghosh, 2013, Panchal et al., 2013, Faisal et al., 2014, Liu et al., 2016a, Tallman et al., 2017].”
- DAMASK – The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale (2018, background): “Therefore, tackling and solving the challenges associated with the inclusion of enhanced and strongly coupled constitutive models and related numerical solvers can serve as a great vehicle for profound progress in the field of Integrated Computational Materials Engineering (ICME) projects [2,3].”
- Machine Learning and Materials Informatics: Recent Applications and Prospects (2017, background): “While a large fraction of such efforts in the current literature has considered relatively simple definitions of the material that included mainly the overall chemical composition of the material, Kalidindi and co-workers [53–56] have recently proposed a new materials data science framework known as”
- Corrosion-fatigue: a review of damage tolerance models (2018, background): “Each material class and response of interest is influenced by a finite, characteristic set of length and time scales associated with key controlling microstructure attributes [187].”