Papers
3
Total Citations
252
H-Index
3
About
Dirk Mohr is a leading figure in the mechanics of materials, with a research focus on the plasticity, fracture, and constitutive modeling of advanced engineering materials. His work bridges experimental mechanics and machine learning, particularly in the context of additively manufactured (AM) and cast metals. Mohr’s major contributions include pioneering high-throughput, robot-assisted mechanical testing to characterize the stochastic properties of materials like AlSi10Mg, as demonstrated in his highly cited 2021 study (75 citations). He is also renowned for integrating neural networks into plasticity theory—his 2020 work on temperature- and rate-dependent modeling of polypropylene has garnered 145 citations, while his recent 2023 paper on transfer learning for recurrent neural network (RNN)-based plasticity models (32 citations) marks a significant step toward data-efficient, path-dependent material modeling. By combining large-scale experiments with advanced computational frameworks, Mohr has enabled more accurate predictions of material behavior under complex loading, directly impacting the design of reliable structural components. His work is essential reading for researchers in solid mechanics, additive manufacturing, and data-driven materials science.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Transfer learning of recurrent neural network‐based plasticity models32 citations · 2023