Microtech

The mixing, separation, and reactions of fluids in the microscale offer numerous advantages. The flows are laminar and form highly stable phase boundaries. These benefits are leveraged in various microscale applications, including fuel cells, inkjet printers, 3D printers, the food industry, personal care products, lab-on-a-chip technology, and more. Insulin pumps for diabetics are a typical example. The challenges for digital twins in the microscale involve the accurate mathematical treatment of low physical diffusions. IANUS is a leader in algorithmic solutions for complex geometries, rheologies, and low diffusions!

You are in good company.

Values

That’s what makes us special.

Precision

We place a strong emphasis on the highest level of precision. In the development of digital twins in the microscale, we are challenged with the need for accurate mathematical treatment of low physical diffusions.

Sustainability

Microtechnology is a guide for the future. Our work is focused on sustainable solutions to reduce environmental impacts, including the development of more environmentally friendly materials, the implementation of energy-efficient processes, and consideration of the product lifecycle.

Algorithmics

We are a leading provider of algorithmic solutions for complex geometries, rheologies, and low diffusions.

Do you want to know how it’s done?

Our experts are eager to take on your challenge!

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Simply IANUS

How can we help you optimize your production, saving both time and costs in the process? Watch the video and get to know IANUS.

What do we actually do?

We support industrial customers with our DeepTech solutions to enhance the technical efficiency of processes and machinery while minimizing resource consumption. Our solutions include AI-based prediction and design assistants that enable quick assessment of various design concepts using the Rapid Prototyping method and reveal potential optimization opportunities. These assistants work based on digital representations of the respective processes, which can be analyzed through fully automated simulations. This allows for the synthesis of large data pools that the AI assistant systems can operate with.

Success stories

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