Why physics-informed AI is becoming a new control layer for industrial assets
The Growing Blind Spot in Asset-Intensive Operations
Across energy, infrastructure, and heavy industry, operators are being asked to stretch assets far beyond their original design life. Equipment is aging, and downtime carries a higher cost than ever. Yet many of the tools used to manage asset health were designed for a very different operating reality.
Engineering teams still rely heavily on design stage models and periodic inspections to understand structural risk. These approaches are precise and trusted, but slow, static, and disconnected from how assets behave in operation. By the time insight arrives, conditions have already changed.
At the same time, real-time monitoring has become ubiquitous. Sensors generate continuous streams of data, dashboards refresh constantly, and alerts trigger automatically.
But most of this data lacks physical meaning.
Sensors can show that something looks different, but not what that difference means for stress, fatigue, or remaining life. The result is a system that is both conservative and blind. Assets are often run below optimal conditions, yet failures still occur.
This gap between engineering truth and operational visibility has become one of the most persistent constraints in asset-intensive industries.
Where Data Alone Falls Short
Much of the recent progress in industrial analytics has been driven by data-centric AI. These systems excel at pattern detection, classification, and prediction, especially when historical data is abundant and failure modes repeat.
Asset integrity does not behave that way.
The most critical failures are rare, cumulative, and governed by physics. Fatigue develops over time. Damage accumulates under changing loads, temperatures, and operating conditions. These processes are not always visible in data until it is too late.
When AI systems are disconnected from these physical realities, their outputs are difficult to trust. Engineers either ignore them or compensate with conservative assumptions. The result is delayed intervention, unnecessary maintenance, or missed risk.
In safety-critical environments, this lack of trust becomes a fundamental barrier to adoption.
Bringing Physics Back into the Loop
Physics‑informed AI takes a different approach.
Rather than learning patterns alone, it embeds physical laws directly into machine learning models. Real-time sensor data is combined with an understanding of how materials behave, how heat flows, and how stress accumulates under real operating conditions.
This is not better monitoring. It is a different way of understanding asset behavior during operation.
Instead of estimating outcomes after the fact, these systems model how an asset is evolving internally as it runs. Temperature is no longer inferred indirectly. Stress is no longer approximated through static assumptions. Engineers gain visibility into conditions that were previously inaccessible without time-consuming simulation.
The result is not just faster insight, but more meaningful insight.
From Simulation to Live Decision Making
Traditional engineering models, such as finite element simulations, are powerful but computationally intensive. They are typically used offline, during design or investigation phases.
Physics-informed AI changes this dynamic.
By combining engineering models with fast-running surrogate models, these systems retain physical fidelity while operating in real time. Insight that once took hours or days can now be generated continuously during operation.
This fundamentally changes how decisions are made.
Operators can evaluate strategies before execution. Control systems can respond to stress conditions as they develop. Assets can be run closer to optimal performance with confidence, rather than buffered by wide safety margins.
The shift is from retrospective analysis to live, physics-grounded decision-making.
Why This Shift Is Happening Now
Several forces are converging.
Advances in computation have reduced the cost of running complex models. Machine learning techniques now allow physical equations to be embedded into efficient neural networks. At the same time, industrial organizations are under pressure to extract more value from existing assets without compromising safety or reliability.
In heat-intensive industries such as power generation and metals processing, even small improvements compound quickly. Better thermal control translates directly into lower fuel consumption, reduced emissions, higher throughput, and improved asset life.
In asset-intensive industries, even small improvements in thermal efficiency or stress management can translate into millions in annual savings at a single site.
What has been missing is a way to deliver this insight continuously, inside live operations.
Why We Invested in MatAlytics
MatAlytics brings physics-informed AI out of the design office and into live operations.
Its CITRUS platform combines high-fidelity thermomechanical modelling with machine-learned surrogate models to deliver real-time predictions of internal temperature, thermal stress, and material behaviour. What traditionally required extensive finite element analysis can now be evaluated in milliseconds, enabling dynamic decision-making during thermally intensive processes. In asset-intensive industries, even small improvements in thermal efficiency or stress management can translate into millions in annual savings at a single site.
What sets this approach apart is not just speed, but relevance.
By preserving physical meaning in real time, MatAlytics provides continuous visibility into stress, fatigue, and damage as assets operate and age. This closes a long-standing gap between simulation accuracy and operational usability.
The company has progressed from over a decade of research into industrial pilots with asset-intensive operators, particularly in power generation and energy infrastructure, where both downtime and over-conservatism carry significant cost.
While early deployments focus on these sectors, the underlying approach extends across industries where thermal and mechanical stress define performance, from metals to aerospace to emerging energy systems.
At Momenta, we focus on execution-grade industrial AI. Technologies that do not just produce insight but change how decisions are made in practice.
MatAlytics represents that shift.
By embedding engineering truth into live decision-making, it enables safer operation, more efficient maintenance, and longer asset life without forcing a trade-off between speed and confidence.
That is why we invested.
Momenta is the leading Industrial Impact® venture capital firm, accelerating innovators across energy, manufacturing, smart spaces, and the supply chain. Our team of deep industry operators has helped scale industry leaders and innovators to improve critical industries, the environment, and people's quality of life for over a decade. PitchBook named Momenta among the world's top ten digital industry venture funds for both 2023 and 2024 in its Global Manager Performance Score League Tables, one of just two European-headquartered VCs to achieve a Top 10 ranking. For more information, please visit: momenta.vc.
