# Computer Methods in Applied Mechanics and Engineering

**Type:** Journals  
**Canonical URL:** https://scholariq.org/journals/computer-methods-in-applied-mechanics-and-engineering/

## Facts

| Field | Value |
| --- | --- |
| Open Access | false |
| ISSN-L | 0045-7825 |
| ISSNs | 0045-7825,1879-2138 |
| OpenAlex ID | https://openalex.org/S40006715 |
| Publisher | Elsevier BV |

## Journal papers

- [A strictly conservative Cartesian cut-cell method for compressible viscous flows on adaptive grids](https://scholariq.org/papers/a-strictly-conservative-cartesian-cut-cell-method-for-compressible-viscous-flows/)
- [Hot and cold strip rolling processes](https://scholariq.org/papers/hot-and-cold-strip-rolling-processes/)
- [Topology optimization of thermal conductive support structures for laser additive manufacturing](https://scholariq.org/papers/topology-optimization-of-thermal-conductive-support-structures-for-laser/)
- [A fully partitioned Lagrangian framework for FSI problems characterized by free surfaces, large solid deformations and displacements, and strong added-mass effects](https://scholariq.org/papers/a-fully-partitioned-lagrangian-framework-for-fsi-problems-characterized-by-free/)
- [Solution of physics-based Bayesian inverse problems with deep generative priors](https://scholariq.org/papers/solution-of-physics-based-bayesian-inverse-problems-with-deep-generative-priors/)
- [Circumventing the solution of inverse problems in mechanics through deep learning: Application to elasticity imaging](https://scholariq.org/papers/circumventing-the-solution-of-inverse-problems-in-mechanics-through-deep/)
- [A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows](https://scholariq.org/papers/a-dimension-reduced-variational-approach-for-solving-physics-based-inverse/)
- [Variationally mimetic operator networks](https://scholariq.org/papers/variationally-mimetic-operator-networks/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.