Making your R&D faster and more reliable

UptimAI is an AI-powered software that makes engineering simulations more realistic while decreasing product development time.


Challenge & Solution

“The process of designing and improving products is time-consuming, expensive, and involves a lot of repetitive work. In addition, solving complex problems with many input variables requires extensive knowledge.”

UptimAI allows engineers to quickly understand the nature of the problems and propose innovative solutions. At the same time, it automates a large part of the work, hence reducing the overall development time and providing more space for creativity.

How it works

UptimAI works as an add-on to simulation tools and is designed to complement engineering simulations with probabilistic and statistical insights. The whole process of using UptimAI is very simple:


Connect UptimAI with your simulation or calculation tool. UptimAI is a non-intrusive solution, so it does not interfere with the simulation platform or the operating system.


Enter the statistical inputs reflecting the real operating conditions into UptimAI. You can design the inputs yourself, use predefined probability distributions or the measurement data.

Machine Learning

The UptimAI Algorithm creates a metamodel of the solved problem, while automatically adapting to it and thus speeding up the whole process. Further analyzes of the problem already use the metamodel, therefore they run much faster.


UptimAI clearly visualizes the links between input variables and the resulting product properties, providing a comprehensive insight into the problem.



Faster design process, more reliable results.

Deeper Insight

Allows to improve the properties of products in the whole range of operating conditions.


Reveals possible problems at each stage of the design process and suggests effective solutions

Lower Costs

Saves up to 90% of the computational time compared to standard approaches or DoE methods

Easy To Use

Does not require advanced mathematical and statistical knowledge of the user


Virtual Vehicle Research GmbH

With UptimAI we conducted an experiment on validation and parametrisation in electro-chemical battery modelling. The challenge was to find at least one suitable parameter set to describe measurements using a “single particle” model. The UptimAI algorithm accelerated the entire process, as the subsequent analysis was carried out much faster using the increasingly improving metamodel. UptimAI gave us a comprehensive insight into the importance of the dynamic parameters, as well as regions of preference and of avoidance. This new knowledge will be integrated into the future parameter estimation and model calibration workflow.

Dr. Matthias K. Scharrer, Battery Research Group

MECAS-ESI s.r.o.

We used UptimAI in two projects for the automotive industry. The first project concerned the properties of the car’s structure and the second one involved the load on the dummy in the event of a car crash. UptimAI confirmed the specific benefits of the method used and its practical applicability in car development. We were satisfied with the quality of the outputs, intensive technical support, and especially with the active and flexible approach of the UptimAI’s team.

Karel Beneš, Senior Technical Consultant

VUT Brno

UptimAI provided us with a deeper insight into problems of the buckling of a pressurized cone with stringers and compressed fiber-reinforced composite panel. The tool quickly described the effects of changes in geometric and material parameters while its outputs clearly showed us the necessary dependencies. UptimAI was easy to couple with the MSC Nastran solver and was easy to use as well.

Jan Šplíchal, Aircraft Structures – Assistant Professor

Idiada a.s.

We cooperated with UptimAI on a calibration of the material model in Abaqus Structural Solver to match with tensile and creep behaviour of the real material. UptimAI provided us with a comprehensive insight into the material parameters relations and their statistical importance. This quantification of the parameter dependencies allowed us to choose an appropriate set of parameters for the intended simulation.

Jakub Cejpek, SIMULIA Consultant

Altran a.s.

We used the UptimAI software on one of our typically solved tasks, and it is undoubtedly an innovative product. Thanks to advanced statistical and probabilistic methods combined with elements of artificial intelligence, it can reveal in the investigated system how design variables affect output values. Thus, it offers the possibility of a deeper understanding of the solved task and can also point to a suitable way to optimize the product or find an innovative solution.

Jan Macháček, CAE Engineer

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