In a recent podcast conversation, John Pascarette, the VP of Product and Market Segments at Monolith, shared valuable insights into how Monolith is empowering engineers to harness the potential of artificial intelligence (AI) and machine learning in the field of engineering.
Monolith is at the forefront of a technological revolution, using AI to simplify and hasten design and testing processes, saving businesses time, money, and effort.
In this blog post, we'll delve into the key takeaways from the podcast and explore how Monolith is making a significant impact on the engineering landscape.
Empowering engineers with self-learning models
Monolith's core mission is to empower engineers to develop self-learning models from their engineering test data. These models allow engineers to gain a deeper understanding of how their designs perform under various conditions and design parameters.
The primary goal is to accelerate new product introductions by leveraging these models to streamline and expedite the testing process.
Challenges in traditional engineering
Traditional engineering processes often rely on physics-based models, which involve complex mathematical equations to simulate the behaviour of products under different conditions.
However, these approaches can become highly intricate and time-consuming, especially for products like rockets, vehicles, and complex nonlinear systems. Monolith's journey began when its founder, Richard Ahlfeld, Ph.D., recognised the limitations of this traditional approach during his postdoc work at NASA.
To accurately model the stress and strains on a rocket during launch, he realised the value of using real-world test data and machine learning to build models that offer a more comprehensive understanding of how designs perform under various conditions.
The impact of Monolith
Monolith's impact is significant and far-reaching, with applications spanning various industries. One notable example is in the electric vehicle sector, particularly in the development of lithium-ion batteries.
These batteries are a critical component of electric vehicles, affecting factors such as range, charging speed, and battery lifespan. Designing and validating these batteries is a complex and time-consuming process.
Monolith's AI-driven approach can reduce the number of tests required by 30% to 70%, significantly expediting the time to market for new electric vehicles.
This not only has implications for business competition but also enhances safety and technology within the automotive industry.
Breaking down AI stigma
John stresses the need to dispel the stigma and fear surrounding AI in engineering. He draws parallels between the impact of AI in engineering and the introduction of spreadsheets for financial analysts.
Just as spreadsheets accelerated financial analysis and made it accessible to more individuals, AI can empower engineers to be more efficient and effective in their work. AI is not a threat to jobs; it's a tool that enhances expertise, ultimately benefiting businesses and industries.
Recognition by engineering matters awards
Monolith's innovative approach to engineering, driven by AI and machine learning, has earned recognition from prominent industry leaders. The potential for Monolith's impact is vast, and an award from Engineering Matters would further validate the real-world application and benefits of AI in engineering.
Conclusion
Monolith is paving the way for a new era in engineering, where AI and machine learning are harnessed to expedite the design and testing of complex products. The impact is already visible, with the potential to revolutionize various industries, from electric vehicles to aerospace.
Monolith is breaking the stigma surrounding AI by demonstrating its tangible benefits, and recognition from awards like Engineering Matters further solidifies its position as a game-changer in the field of engineering.
Check out the full podcast here: #259 Engineering matters awards – innovation, part 3.
