After working extensively with POMAI, we can confidently say the technology works. The AI-driven optimization produces real results—lighter, stronger, and more efficient components, with clear cost reductions when transitioning from traditional manufacturing to additive methods. But here’s the thing: while the technology itself is impressive, using it still requires effort. Right now, POMAI is effective but not exactly effortless for the end user. That’s what needs to change in the next phase.

POMAI has successfully demonstrated its ability to optimize antenna brackets and other aerospace components. The results speak for themselves: mass reductions, better performance characteristics, and cost savings. But working through the process—setting constraints, refining the mesh, validating results—takes time and expertise. While the software achieves its technical objectives, it still asks too much of the user.

The challenge is that topology optimization and probabilistic modeling inherently involve complexity. Engineers using POMAI need to carefully define parameters, interpret optimization outputs, and sometimes manually refine designs before final production. This isn’t a problem for an experienced analyst, but if we want more widespread adoption, we need to make the process more intuitive.

## What Needs to Improve?

### Streamlined User Interface & Workflow

Right now, using POMAI feels more like an expert tool than an accessible solution. The goal moving forward should be simplifying the workflow—reducing the number of steps required and improving automation in setting up optimization parameters.

### Automated Post-Processing

Currently, some manual intervention is required to clean up the optimized geometries before they are ready for manufacturing. Future iterations should integrate automated post-processing features to further reduce human effort. This is actually quite hard, since people ultimately need to do some basic decision-making, and automating this is tricky at best.

### Better Integration with Existing Design Workflows

Engineers aren’t working in isolation—they already use CAD and simulation software. POMAI needs to better integrate with these tools, reducing the friction of moving between platforms and ensuring optimization results can be directly implemented.

## The Road Ahead

For AI-driven design tools like POMAI to be truly useful, they need to be as easy to use as possible. The next phase of development should focus on reducing complexity. We strongly believe that having users involved in each stage of development is key to achieving this.

If you’ve worked with AI-driven optimization tools before, you probably know the feeling: incredible results, but sometimes frustrating execution. The challenge now is clear—how do we take something that already works and make it effortless? That’s what’s next for POMAI.


The next evolution of POMAI focuses on making AI-driven optimization tools easier to use by streamlining workflows, automating post-processing, and improving integration.

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Where We Are Now

After months of work, long testing sessions, and a few unexpected turns, We’re excited to share where ICARUS ARMOR is headed next. This project, originally supported by the ESA Spark Funding Program, has been a fascinating deep dive into how we can better understand and support human performance in extreme environments. Now, as we move into the next phase, We’re particularly eager to focus on working closely with local academia to refine and expand our findings.

## Lessons from the First Phase

One of the biggest takeaways from this project so far has been finding the right balance between accuracy and usability. Early on, we used a broad set of wearable sensors, including respiratory masks to track oxygen and CO2 exchange. However, after multiple trials and discussions with the people we collaborated with, we discovered that some of these measurements weren’t essential. We could still get highly useful physiological data while making the system far less intrusive for users.

A major win has been keeping the system hardware-agnostic. This flexibility allows it to integrate with different wearables, making it adaptable for a range of users and environments. Working with local university labs has been instrumental in testing this adaptability and refining our approach. Their feedback has helped us ensure that the system is both effective and practical.

## What's Next?

The next phase of ICARUS ARMOR will focus on three key areas:

### Collaborating with Local Academia to Improve the Digital Twin Model

The digital twin system, which builds a personalized physiological model based on biosignals, has been a cornerstone of this project. We’re eager to work more closely with university researchers to fine-tune these models, ensuring they can be applied in real-time operational settings with greater precision.

### Expanding Field Testing with Partnerships

We’ve already worked with firefighter brigades and conducted trials with ESA astronauts. Now, we’re looking to partner with local academic institutions to further validate the system. Working directly with university researchers and students will provide fresh insights.

### Advancing Real-Time Monitoring and Alerting

One of our goals is to enhance the software’s practical applications. The system already tracks physiological states, but we want to refine the alert mechanisms and intervention strategies based on academic research. This will ensure that ICARUS ARMOR doesn’t just analyze data but actively helps individuals make safer decisions in high-stress situations.

## A Vision for the Future

Ultimately, our goal is to make ICARUS ARMOR an intuitive, easy-to-use system that supports people in extreme conditions. We believe the only way to do this is by working closely with real users. We hope to refine the technology and bridge the gap between research and real-world application.

There’s still a lot of work ahead, but we’re excited about the direction we’re taking!

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ICARUS ARMOR is evolving with enhanced digital twin modeling, expanded field testing, and improved real-time monitoring to support human performance in extreme environments.

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The Next Phase of ICARUS ARMOR - Advancing Human Performance Monitoring in Extreme Environments

**AI Isn’t Magic—It’s a Tool (If You Know How to Use It!)**

*Martin Kubicek*

We live in an era where AI is often seen as a magical entity—an all-knowing machine that, when given data, will spit out the perfect answer. If only it were that simple! The truth is, AI is only as good as the problem you define for it. And here’s where things get tricky: we often don’t know what we don’t know.

### The Importance of Assumptions

Let’s start with a seemingly simple question: *What is 1 + 1?*

Most people would say 2. But is that always the right answer? Well, that depends. In the decimal system, sure, 1 + 1 = 2. But in binary, 1 + 1 equals 10. If we’re talking about biological reproduction, sometimes 1 + 1 equals 3! A mathematician might ask whether the numbers are real or imaginary. A programmer might wonder if we’re dealing with integers, floats, or booleans. An engineer might ask about tolerances.

See the problem? The answer isn’t always straightforward—it depends on the assumptions we make. And assumptions are the foundation of any AI system.

### AI Needs Clear Instructions

I once had a friend who dismissed mathematical software as useless because it failed to simplify an equation for him. He entered a command—*simplify*—but the equation came back unchanged. The issue wasn’t the software; it was that he hadn’t told it *how* to simplify. Should it assume only real numbers? Should it allow imaginary numbers? The software wasn’t dumb—it just needed clearer instructions.

AI works the same way. It doesn’t *think* like humans do. It doesn’t make assumptions unless we explicitly provide them. So when we use AI, we need to be crystal clear about what we’re asking and what constraints we’re working within.

### Defining the Problem Correctly

Let’s say we want to predict how a satellite behaves based on temperature data. If we just ask an AI model, *“Hey, predict satellite behavior based on temperature,”* we might not get useful results. Why? Because we haven’t provided the right context. A better approach would be something like:

> *“Hey AI, I have temperature data from a satellite, and I want to predict future readings under new conditions. But remember, this is space—some sensors might fail due to the harsh environment. Also, solar radiation plays a huge role, and don’t forget about heat reflecting from Earth’s atmosphere.”*

The more context and boundary conditions we provide, the more useful the AI’s answer will be.

### AI is Smart—But You Need to Be Smarter

At the end of the day, AI is a tool—an incredibly powerful one, but still just a tool. If it gives an answer that seems *stupid*, it’s usually because we didn’t frame the problem correctly. AI isn’t here to read our minds; it’s here to help us make sense of data. But to get the best results, we need to understand what we want, why we want it, and the conditions we’re working with.

So the next time you use AI, don’t just expect magic. Take a step back, think through your problem, and set the right expectations. You’ll be surprised how much more effective AI becomes when it actually knows what you’re asking for!



AI is often seen as a magical entity—just feed it data, and it will deliver the perfect answer. If only it were that simple!

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AI Isn’t Magic—It’s a Tool (If You Know How to Use It!)

## A Problem of Probability Density Function Estimation for Large Dimensional Spaces With Many Low-Influential Dimensions
*Jergus Suja, Martin Kubicek and Tomas Koutnik*

### Abstract
All engineering problems consider uncertainties. These range from small production uncertainties to large-scale uncertainties coming from outside, such as variable wind speed or sunlight. Currently, modern methods for uncertainty propagation have large difficulties with estimation of statistics for large-scale problems which considers hundreds of these uncertain parameters. Due to the complexity of the problem and limitations of the modern methods, a common approach for modelling large scale problems is to select a few important parameters and model statistics for these parameters. However, this can lead to an important problem. In this paper, an application of the UptimAI’s UQ propagation algorithm is used to discuss a new problem arising from very high dimensional spaces where a large number of parameters have negligible impact on the final solution. In other words, when a problem consists of a great number of uncertain design parameters, common practice is to focus on the most important ones and neglect the non-influential ones. However, a combination of a great number of noninfluential parameters can lead to completely different results. This is especially a problem for modelling large dimensional statistical models where a common approach is to perform sensitivity analysis and neglect the non-influential variables, i.e. set the non-influential variables to nominal value. Therefore, using a common approach of neglecting the non-influential variables could lead to a dramatic error and hence, we call this problem ”many times nothing killed a horse”. This problem cannot be observed for cases with a small number of design parameters, which are commonly solved in statistical modelling. The reason for this issue is that the combined influence of neglected variables is extremely small and such that has no influence on the final output. Application of the UptimAl’s UQ propagation algorithm to modern engineering problems and the possibilities of mitigation of the cumulative influence of non-influential parameters is discussed in detail. The problem is shown on a case of economic load dispatch (ELD) problem which consist of 140 dimensions [1]. To this problem was applied UptimAI’s UQ propagation algorithm to obtain accurate statistics for the problem and to get deeper insight into the statistics. Using the accurate model obtained by UptimAI’s algorithm, we compare statistics of using only important variables and using all variables. This lead to a significant difference between results and such that put a large question mark on standard approach. The obtained results are validated with the Monte Carlo simulation applied directly to ELD problem. Application of UptimAl’s UQ propagation algorithm to modern engineering problems and the possibilities of mitigation of the cumulative influence of non-influential parameters is discussed in detail.

Link to a full article [here](https://www.scipedia.com/public/Suja_et_al_2021a).

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Problem of Large Dimensional Spaces

Take advantage of the current situation, invest in internal process improvement and be ready for upcoming market changes. **Market leaders see this rather difficult time as an opportunity to develop and become stronger.** Once the challenges are settled down, those who already implemented advanced technological and automated solutions will be the ones with a competitive advantage.

When almost the entire world is working from home and business may seem to slow down, making everything as effective as possible is crucial. From everyday tasks to top-level decisions, each activity is being re-evaluated for its actual value to the whole company and future steps. **Wrong decisions and double work are now more costly than ever. **The design process in manufacturing needs to be optimized to prevent failures that eventually lead to wasted resources.

**Our one-of-a-kind solution has proven to reduce design costs, scrap rates and to save many hours of work.** Being able to redistribute these resources smartly elsewhere is vital, especially in the present circumstances.

**We have shifted to fully remote mode and we are able to implement UptimAI virtually everywhere.** We are ready to help you fasten and optimize the design process. Take the first step today for an efficient process tomorrow.

[Get in touch with us and learn more](https://uptim.ai/). 

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Why invest in AI technology now?

The evolution of the UptimAI software has begun at the University of Strathclyde in Glasgow, where its prototype was used by researchers to identify impact trajectories of the space debris. **For the first time, it became possible to create complex mathematical models with an even lower amount of simulations than is the number of problem parameters.** To prove technology’s abilities on practical engineering problems, it was used for the analysis of the novel design of the flapped airfoil for commuter aircraft. The model of the problem created by UptimAI confirmed that the proposed flap concept has large potential to **improve** the airplane’s flight performance and shorten the landing distance. Compared to commonly used Design of Experiments methods, the UptimAI approach **saved more than 450 hours of computational time.** Moreover, it allows designers to easily find the direct way to stable aerodynamic characteristics which can be relied upon even if the flap is being deformed under loading. The article was written by CzechInvest with the help of our Head of Application, Tomáš Koutník.

Read the full article [here](https://www.czechinvest.org/en/Homepage/News/September-2020/What-does-space-do-for-the-aero?fbclid=IwAR2l9jMvcs-aW81prkCUC6tLFfqYZHgJWSrHOOMIUK1AETcaFF3uHawAIA4).

*Source: European Space Agency*

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What does space do for the aero?

The whole project started as an off-spin of Martin’s projects during his PhD studies at Starthclyde University. This new algorithm for the propagation of uncertainties was based on a fundamentally new approach to meta-modeling (https://en.wikipedia.org/wiki/Metamodeling), which was later targeted to engineering problems. It turned out that the developed code was very efficient and very robust. Moreover, the technique provided some new insight into the problem and helped to inform a deeper understanding of the problem. It’s first application was the uncertainty propagation for re-entry of orbital debris, where it has shown the importance of statistical propagation. Results clearly showed that a common assumption of ‘Plus-minus’ around the result is completely wrong as you can see in the picture below.

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The statistics have a profound influence on the expected result and sometimes it can create some famous quotes such as ‘Houston, we have a problem’. Well, engineers were probably not very happy about this infamous space catastrophe.Moreover, the result was something unexpected even for the team who was working on the re-entry case. We tested the result with an experiment by throwing small balls into the air and observing their impact locations. It turns out that the results of the experiment were very similar to our algorithm, which proved that our algorithm worked very well.

The second case was the re-entry of GOCE and its flight parameters . Here again the algorithm showed its strengths and some interesting ‘wow’ moments about the re-entry. However, it showed a very important aspect of the algorithm, that it can work very efficiently for virtually any problem. Hence, it was decided to transform the algorithm into something industry can use to improve their products.

In the first year, the algorithm started to change to suit industrial needs. It was a very turbulent time, but after many hours of programming, testing and developing, an improved version of the algorithm was created. This new algorithm was used for the collapse of the car box-beam, which was done in cooperation with MECAS ESI in Pilsen. Again, the algorithm proved its robustness and showed that it is working very well even for such complex cases. More importantly, we came up with some interesting ideas on how to provide engineers with even deeper insight. This case impressed Skoda Auto and we started to cooperate on passive safety of cars.

After the first year and with a successful application of our code in industry, we were accepted into ESA Incubator in Prague (http://www.esa-bic.cz). Empowered with new ideas, experienced with industrial problems and full of enthusiasm, we started to develop a new industry target platform. After two years of successful incubation, we can proudly say that we developed a tool, which can tackle even the most difficult cases with an efficiency never seen before. Moreover, apart from standard analysis, our tool provides unique insight, which is only available due to our unique approach. Our tool already helps engineers to develop a product, which is more reliable, efficient and ahead of it’s competition.

Today, industry and academia are using our tool, but we will not stop there. We have plenty of new ideas on how to improve our code in its efficiency and even more ideas on how to provide engineers additional insight to produce a better product – so stay tuned.


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The story behind Uptimai

Blog

We are happy to share that we have been nominated as one of the top Machine Learning companies in the Czech Republic by Best Startup EU!

In the published article, Best Startup EU picks the companies that are taking approaches to innovate the Machine Learning industry in all kinds of industries, and selects the companies that excel in one of these categories:
- Innovation
- Growth
- Management
- Societal impact

You can read the full article [here](https://beststartup.eu/41-top-machine-learning-startups-and-companies-in-czech-republic/).

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Top ML company in Czech Republic

Uptimai has been selected as one of the 28 most innovative Czech Republic companies that use Machine Learning Technology by Futurology Life.

Futurology Life is a media company based in London that aims to boost inbound investment into innovative companies and startups by addressing the information asymmetry between small innovative startups and institutional investors.

The companies were selected by their performance in the following categories: Innovation, Growth, Management and Social Impact.

You can check the complete article for more information [here](https://futurology.life/28-most-innovative-czech-republic-based-machine-learning-companies/).

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Innovative Machine Learning Company

Our CEO Martin Kubicek and Head Developer Jiri Otoupal visited the University of Defense in Brno. During the visit our team made a presentation to show the capabilities of our software and engineering team. Our cutting-edge Uncertainty Quantification approach leads to deeper insights and better designs for all the different performance conditions for both civil and military purposes.

Events like this one allows to create links between the civilian and the military industry, and to reinforce our commitment with the academia.

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Visit to the University of Defense

Our CEO Martin Kubicek and Head Developer Jiří Otoupal attended the third **Czech-American Defense and Cybersecurity Forum in Prague.**

In the beginning, Deputy Minister Kopečný and many other government & security officials emphasized how useful and important these regular meetings are. **If companies want to keep pace in technological progress they have to find a way to collaborate with a variety of organizations** and such events are a great way to do so.

The event was designed to strengthen bilateral cooperation between the US and the Czech Republic. It brought together the U.S and Czech defense suppliers with senior defense and industry leaders **to share important information, and to forge and develop new connections.** The collaboration between the two countries is obviously paying off because the export of military materials is **increasing in the last decade.**

It was a high-profile opportunity for us so we are glad that we were given the opportunity to take part.

![news12_image1.jpg](https://uptimai-website.s3.eu-central-1.amazonaws.com/news12_image1_57b3f2ae7c.jpg)
*Our staff and ambassador Stephen B. King*

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Czech-American Defense and Cybersecurity Forum

The world is dealing with the global pandemic longer than we expected and the impact of COVID-19 is inevitable. Although the times are unpleasant and connected to a lot of potential obstacles **it is necessary to be even more creative and effective.** We are at that point, where the production is in a state of stagnation but the new products need to be in the continual process of development. **Constant innovation is crucial when it comes to the long-term survival of a business.**

All of us have to be in some way prepared for future events and think further when the situation will get better. That’s the reason why we decided to step up to help in these difficult times by **making our product more affordable, and accessible to support your development and be part of your success.**

Today´s post is dedicated to an **exclusive discount**. How does it work? **We offer a 60 % off subscription fee for all new SME clients (up to 500 employees)** for the first year without any commitments. [Request a demo](https://uptim.ai/) and we will be happy to provide you more information.


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COVID-19 SMB Discount

We are happy to announce that UptimAI has become a part of **Composites United cluster!**

Composites United is a Berlin-based association for **sustainable fiber-based multi-material lightweight construction** with a farseeing vision and a progressive mission. The cluster brings together about 400 members from various industries, regions and countries, united by the same goal of developing new approaches and solutions for fiber-based constructions. A membership in the Composites United clusters gives members the opportunity of participation in research and development projects, access to new international markets, help with funding and promotion and many more.

The main goals of UptimAI as fresh members are cooperating with the wide network of Composites United, meeting **new potential partners and clients**, and presenting to them our **cutting-edge solution**.

Check the [Composites-United website](https://composites-united.com/).

Learn more about [how our solution works](https://uptim.ai/) and request a demo to give it a try!

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We are a member of Composites United!

UptimAI company was incubated in the **European Space Agency Business Incubator** in Prague to enable the use of our cutting-edge algorithm for a wide range of industrial applications. **At the moment, we are able to connect UptimAI with any CAE simulation platform without any limitations.** In cooperation with ESA BIC Prague, we created a video to show you our Uncertainty Quantification solution in more detail.

[Get in touch](https://uptim.ai/) with us to discuss possible use-cases for your CAE challenges. **You will be amazed by the efficiency of our product!**

![esa-bic-video](https://uptimai-website.s3.eu-central-1.amazonaws.com/ESA_UPtimAI.mp4)
Video: [https://www.youtube.com/watch?v=dEKTxXobQDg](https://www.youtube.com/watch?v=dEKTxXobQDg)






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ESA BIC Prague introduces UptimAI

UptimAI was shortlisted for the TOP30 in a startup competition organized by Vodafone. It is a really great opportunity for us to show our solution to the jury and get feedback from experienced entrepreneurs. **Additionally, there is a chance to win valuable prizes or up to €3.75M investment!**

More information about the competition can be found [here](https://napadroku.cz/en/). **Wish us luck! **

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Vodafone Idea of the Year TOP30

We got into PowerUp! Country Final Czechia, where 8 startups are going to pitch and compete to advance to Grand Final, multi-million Euro investments, and wide support from InnoEnergy.

PowerUp! is the challenge for start-ups, scale-ups, and SMEs from Central and Eastern Europe whose products in the fields of energy, smart technologies, mobility are solving today’s problems with tomorrow’s technology.

Who to expect? Ibatt.energy, BatteryCheck, ENMON Technologies, Pocket Virtuality, UptimAI, Topíte, Skymaps, and Mileus.

**[REGISTER for the event](https://www.eventbrite.com/e/virtual-powerup-country-final-czechia-tickets-102906020678) to save your spot.** Even the online seats are limited!

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PowerUp! Country Final

We are honored to announce that we will take part in **Industry Space Days 2020 (ISD).** This virtual event will take place on 16-17th September 2020.

The ISD is an event organized by ESA which brings together **important European players in the space sector through B2B meetings and conferences.**

The main purpose is to connect and increase involvement in space activities. Companies are encouraged to share their visions and ideas to produce better results. So it provides us a **great chance** to introduce our product, meet potential clients, or create new business collaboration.

Stay tuned and catch us there!

Check the official websites [https://isd.esa.int/](https://isd.esa.int/)

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ISD SPACE DAYS 2020

We’ve been named one of **@hellotmrc**’s Deep Tech Pioneers, from over 5,000 applications from 128 countries!

HELLO TOMORROW is an organization that identifies, and analyses innovation trends & dynamics leveraging experts. It connects the best minds and builds a worldwide community of science entrepreneurs, investors, corporates and academics to work together to untap the enormous potential of deep technologies. The most anticipated technology event in the world will happen this fall and we will be a part of the Deep Tech week.

Upcoming Summit will cover four major topics: Space, Planet, Human and Machine.

**Come and meet us at the Hello Tomorrow Global Summit in Paris on the 12th – 13th of March 2020!**

**EDIT: POSTPONED TO 22nd – 23rd of October 2020.** We will keep you posted. **<u>[Find more information here](link)</u>**

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We are Deep Tech Pioneers!

At the end of 2019, we participated in an acceleration program that supported our company’s growth significantly. With the investment from Lighthouse Ventures, we have been able to advance the tool development, rebrand and expand the business.

“**Martin Kubíček has developed a unique product that is ten times faster and significantly more accurate than similar solutions on the market.** Thanks to our acceleration the program, we want to help him turn technology into business success on the international market,” explained Michal Zálešák, the Managing Partner at Lighthouse Ventures.

UptimAI offers full automation and simple connection to standard simulation tools. This leads to high efficiency while offering deeper insights and a different perspective on product optimization. **This is a revolution for the simulation area.** One tool that combines an otherwise lengthy process of simulation set-up, code synchronization, and the creation of a simplified model. Thanks to the unique algorithm, for example, engines are more efficient, cars safer or bridge structures more stable. The solution can be applied in a wide range of industries. Use for the solution can be limitless, for instance, aircraft and spaceships manufacturing, engineering and medicine.

“Since we are engineers by profession, **we understand that existing optimization methods are not applicable to conventional engineering designs.** Instead of a clear optimum, our algorithm will offer the engineer a wider field of choice, which is much more practical than the standard approach,” says Martin.

One of the successful pilot projects that was our jump start for the investment was executed for Škoda Auto in Mladá Boleslav, where our startup worked on the so-called box beam. This is the part of the car that absorbs impact energy and significantly increases the chances of survival in an accident.

“We helped to improve the element to be resistant to many directions of impact and not just to the front impact. We also helped set the element so that its absorbed energy is not affected by external circumstances,” says Martin. Since the pilot, we have continued our cooperation with Škoda.

The article was written by Czech Crunch, which interviewed Martin Kubíček, the founder of UptimAI.

**Read the full article [here](https://cc.cz/vyvinul-algoritmus-ktery-pomaha-skodovce-a-zachranuje-zivoty-ted-na-nej-martin-kubicek-ziskava-miliony-korun/)** (Czech version only).

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News & Blog

News