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AI AND MACHINE LEARNING FOR ENGINEERING

Bridging essential technology gaps by augmenting traditional engineering methods with AI/ML techniques, ATA Engineering provides quick, cost-effective solutions for our customers.

Unlocking Breakthrough Engineering Solutions with AI/ML-Driven Insights and Multidisciplinary Expertise

Over the past decade of delivering engineering methods development solutions to our customers, ATA Engineering’s multidisciplinary expertise in traditional engineering fields and innovations in AI/ML algorithms have provided unique value in delivering rapid, trusted insights in the face of challenges once thought to be intractable due to computational costs for physics-based simulation or analysis of large volumes of empirical data.

How Does ATA Utilize AI & ML?

While many organizations tout the transformative, all-encompassing possibilities of AI/ML technologies for the world of tomorrow, ATA’s approach to harnessing AI/ML focuses on delivering solutions today using tools and methods tailored to specific customer challenges.

When our customers struggle with bottlenecks in their engineering processes that hinder their cost and schedule performance or their ability to leverage advancements in modeling and simulation or experimental measurements, ATA takes a holistic, multidisciplinary approach to identifying innovative engineering methods. ATA engineers apply their wide-ranging expertise in physics-based modeling and simulation, data science, AI/ML algorithms, testing methods, and statistical analysis. Oftentimes these efforts involve leveraging academic research and implementing new technologies in customer program settings or as applied research solutions using rigorous phased development approaches and clearly defined verification and validation milestones to mitigate technical risks.

Our Custom AI/ML Solutions

When developing custom AI/ML solutions to address specific engineering challenges, ATA focuses on providing our customers with insights into the utility and trustworthiness of models by leveraging advancements in uncertainty quantification, documenting the deployed algorithms, providing clear direction on the required investments in network training data, and defining the bounds of applicability for AI/ML models where they are able to provide high confidence in the rapid insights they deliver. This approach differs from many state-of-the-art commercial ML modeling tools that offer a “black box” user experience shrouded in proprietary algorithms and underlying training databases with potentially questionable relevance to the problem at hand.

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