PI Probaligence Gmbh


We looking forward to attend at the European Automotive & Plastic Coating Event in Stuttgart 10.-11. Sep 2024 where we present our research project together with Mankiewicz , Fraunhofer IFAM, HS Niederrhein, Füll Lab Automation and AOM Systems and how our software STOCHOS is able to use real process data for ML-guided coating development and production process control.


From 13. – 14. Juni, we participated on the Smart Paint Factory Alliance’s 2nd Workference in Bremen. Find here more details about the Workference.


We are looking forward to the the BiotechX Webinar “How in silico technologies could accelerate and amplify AI deployement for pharmaceutical applications” on 19th June.


Join us at this year’s CADFEM Conference in Rapperswil on 20th June. 


Read our article “AI for Engineering” in the engineering journal Der Konstrukteur

In an article published by Der Konstrukteur, we showcase the benefits of our software development in the engineering sector,


Our presentation on multi-fidelity was well received at the CADFEM Conference 2024. We are pleased about the numerous new contacts and the many specialists who recognized our advantages so quickly.


PI Probaligence to participate on the CADFEM Conference 2024 in April

PI particitpats with an own booth and 2 presentations on the CADFEM Conference 2024 in Darmstadt, Germany.


Multi-scale modelling and machine learning based simulation of
the mechanical behaviour of graphite-resin composites


We will present our recent developments in Machine Learning for Engineering solutions on Apr. 4th in Brasov, Romania and hope for fruitful discussions with leading engineering companies.



Project meeting with the project partners at the Fraunhofer Institut in Bremen

Together with partners from industry, research and universities, we develop a Machine-Learning guided coating formulation using high throughput experimentation.


CADFEM has brought PI Probaligence on board as a partner with outstanding solutions and expertise to provide customers with targeted support as they move into the world of AI.


By changing our default backend the already fast training time of our ML algorithms STOCHOS and PI-BO can be almost improved by factor 2. 


The MIT Technology Review published a new study about the carbon footprint of general, giant ANNs used for example for picture generation and energy needed for the model usage.


Again PI-BO was the best performing optimizer. Similar to BOSCH this time ZF came to the same conclusion.


We are proud that only after only 4 adaptations we came already very close and partly even better than the results of commercial color matching software using standard formulas


On 19.10.2023, the official kick-off event of the “Green Tech Innovation Competition” took place in Berlin. The aim of the projects funded by the BMWK in this context is to use sustainable digital technologies to contribute to the achievement of German and European climate and environmental protection goals within the framework of the 2030 Agenda.


Together with our partners from Humotion GmbH and the Medical School Hamburg PI joined the SMHS 2023 to present our Smart Injury Project.


Project: Na, Logisch! : Sustainable paint development through digital technologies for climate and environmental protection

Pi is proud to participate on the project Na, Logisch!


We are very happy and proud of the feedback we received from BOSCH on the Ansys WOST Conference last week for our PI-BO Optimizer.


Ansys integrates Stochos in optiSLang

Ansys Inc. an US developer of  CAE/multiphysics engineering simulation software for product design, testing and operation integrades part of STOCHOS in their process integration & design optimization tool optiSLang.


The “Smart Injury Prevention” research project starts with the aim of using artificial intelligence (AI) to detect potential running injuries in a timely manner and thereby prevent them. For this purpose, the specialist for body-hugging sensors Humotion GmbH and the software expert PI Probaligence GmbH, together with the University of Hamburg, are developing intelligent software for the personalized prediction of running injuries.

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