DYNAMORE HOLDING GMBH

Their core work

DYNAmore is a Stuttgart-based engineering software and simulation company, best known as a major European distributor and developer of the LS-DYNA finite element analysis software — a standard tool for crash simulation, structural dynamics, and forming analysis across automotive and aerospace industries. Their H2020 participation reflects two distinct competencies: structural simulation of composite aerospace materials under extreme dynamic loads (EXTREME project), and the application of machine learning and evolutionary algorithms to engineering optimization problems (ECOLE project). In both projects they acted as an industry contributor, bringing production-grade simulation knowledge into academic-industrial consortia. This makes them a rare SME that bridges high-end commercial simulation tooling with applied research in AI-driven engineering design.

What they specialise in

How they've shifted over time

In their first H2020 engagement (EXTREME, 2015–2019), DYNAmore contributed to fundamental structural mechanics research — specifically how composite aerospace materials behave under extreme dynamic loads, a natural extension of their simulation software expertise. By their second project (ECOLE, 2018–2022), the focus shifted decisively toward intelligent computation: nature-inspired optimisation, evolutionary algorithms, machine learning, and experience-based learning applied to engineering problems. This trajectory suggests the company is moving from being a simulation tool provider toward becoming a provider of AI-augmented engineering optimisation, where past simulation runs feed machine learning models that improve future designs.

DYNAmore appears to be positioning at the intersection of high-fidelity simulation and machine learning — partners seeking expertise in training ML models on engineering simulation data, or applying evolutionary algorithms to structural design optimisation, will find them an increasingly relevant SME collaborator.

How they like to work

DYNAmore has never led an H2020 project as coordinator — they enter consortia as a specialist industry partner, contributing domain knowledge and tooling rather than project management. With 20 unique partners across just 2 projects, they have operated within mid-to-large consortia, suggesting they are comfortable in multi-partner environments while playing a focused, defined role. For future collaborators, this means DYNAmore is likely to be a reliable, low-overhead partner that delivers specific technical contributions without competing for project leadership.

DYNAmore has built connections with 20 unique consortium partners across 7 countries through just 2 projects, indicating dense multi-partner consortia rather than bilateral collaborations. Their network spans European aerospace, transport, and research institutions, consistent with an engineering software company that interfaces with both academia and industry clients.

What sets them apart

DYNAmore is unusual among EU research participants because it is a commercial software company — not a university or research institute — that brings production-grade simulation tools and real-world engineering datasets into research consortia. This makes them valuable for projects that need to demonstrate industrial applicability: they can test methods on genuine engineering problems and validate results against commercial simulation benchmarks. For ML or optimisation research projects seeking an industry validation partner with deep finite element analysis credibility, DYNAmore fills a niche few German SMEs can.

Highlights from their portfolio

• EXTREME
  Their only directly funded H2020 project (EUR 144,300), placing DYNAmore at the frontier of aerospace composite structural simulation — directly aligned with their core LS-DYNA simulation business.
• ECOLE
  An MSCA Innovative Training Network on experience-based machine learning for engineering optimisation, where DYNAmore's third-party role suggests they provided industry datasets or hosted fellows — signalling an early move into AI-augmented simulation.