Across every frontier of science, engineering and medicine, the convergence of computational science, high-performance computing and artificial intelligence is not merely accelerating research; it is transforming what questions we can ask, what problems we can solve and how quickly solutions can reach the people who need it most.

This transformation rests on a foundation that is deeper than any single technology. For centuries, science advanced on two great pillars: theory and experiment. In the latter half of the 20th century, computation emerged as a third pillar: a new way of knowing that could simulate physical reality, test hypotheses across vast parameter spaces and reveal structure hidden in complexity. 

Over the past few years, with the rise of AI, we are witnessing the emergence of a fourth pillar, one that complements and extends our theoretical understanding and opens pathways to discovery that none of the earlier pillars could reach alone. The most powerful science of our era lives at the intersection of all four, and this is precisely what the Michigan Institute for Computational Discovery & Engineering (MICDE) is pursuing in collaboration with other units across campus and our external partners.

Michigan-led discoveries

To be clear, U-M researchers are already doing extraordinary work enabled by advanced computing:

At Michigan Medicine, neurosurgeon Todd Hollon develops AI to distinguish tumor from healthy tissue in seconds during brain cancer surgery. Previously, residual tumor was missed in 1 out of every 4 patients; with AI assistance, that figure has dropped to 1 in 25.

Professors Charles Brooks and Jonathan Sexton and their research groups are using computational modeling and AI to simulate molecular interactions, predict toxicity and identify promising compounds before they ever reach a patient. What once required years of trial and error in the laboratory can now be explored computationally in a fraction of the time.

On the other end of the scale, Professor Dragan Huterer and an international team of nearly a thousand scientists are using computational modeling and machine learning to map the evolution of tens of millions of galaxies, searching for evidence that dark energy (the mysterious force accelerating the expansion of the universe) may itself be evolving over time. If confirmed, this discovery will reshape our understanding of the cosmos.

Closer to Earth, computation is reshaping the engineering of clean energy. Professor Venkat Viswanathan’s group is using computation and AI to develop lightweight, durable batteries powerful enough for electrically powered vertical takeoff and landing vehicles. 

And Professor Tuija Pulkkinen’s team models the violent physics of space weather: streams of charged plasma hurtling from the sun at hundreds of kilometers per second, to protect the satellites, power grids, and navigation systems on which modern life depends. 

My own students are using generative AI to predict extreme weather events such as Category 5 hurricanes. But where does the data to train these models come from? High-fidelity simulations, assimilating measured data from thousands of sensors and satellites. This is, again, a testament to a deeper truth: Computation has become the connective tissue of modern science.

Impact and concerns

This work is happening today at U-M, but our independent computational capacity is limited, leaving many critical research problems unaddressed.

To remain competitive globally and fully realize our public mission, we must expand that capacity. This need is why U-M is planning a new supercomputing research center in Ypsilanti Township in partnership with Los Alamos National Laboratory and the state of Michigan. Transformational supercomputing facilities are the tools needed for our state and nation to expand research aimed at solving some of our most pressing challenges.

The new supercomputing center provides the scale required for larger simulations, faster discovery, deeper collaboration and more ambitious research.

Just as importantly, it enables a $1.25 billion investment in high-talent careers and scientific discovery right here in Michigan, helping build a new sector of advanced research and innovation across the state. In fact, some of the aforementioned research has been funded by Los Alamos National Laboratories. As we celebrate these advances, we must be honest about the challenges they bring.

As the appetite for computation grows, so does the environmental footprint, though not nearly as demanding as models trained by leading commercial AI companies. Some might argue this is a reason to slow down. I argue the opposite: It is precisely why the University of Michigan must lead. The question is not whether these technologies will be developed and deployed at scale (they will). The question is whether that development will be guided by institutions committed to the public good, or left entirely to those with narrower interests.

There is also a deeper irony worth noting. These computational tools that consume energy are also the tools we need to solve the clean energy problem: to design better wind farms, to model fusion plasmas, to optimize the power grid and to invent the next generation of energy-efficient computing itself.

U-M is choosing to lead on all of these fronts. Our computer engineers are pioneering energy-efficient computing architectures and developing algorithms that achieve more with fewer computational resources. We do not run from hard problems. We approach them with intent and deliver solutions.

Our responsibility

The challenges facing science and society today are not neatly contained within any single discipline. Climate change, pandemic preparedness, equitable healthcare, sustainable energy, national security, sustainable computing: Each demands the integration of theory, experiment, computation and data-driven discovery. A university is the only institution in society deep enough and broad enough to hold all of these modes of inquiry together and to direct them toward the common good.

There is a human dimension to this work that should not be overlooked.

Every model that identifies a drug candidate faster is a patient who may receive treatment sooner. An AI system that guides a surgeon’s hand more precisely is a family whose world is not shattered by preventable harm. A simulation that forecasts a solar storm, optimizes a wind farm or predicts the failure of a building is a community better protected and better served.

The computations are seemingly abstract but their consequences are profoundly human. This new center ensures that Michigan not only participates in the next era of discovery, but leads it, responsibly and in service of the public good.

— Karthik Duraisamy is center director for the Michigan Institute for Computational Discovery and Engineering Research. He is the Arthur B. Modine Professor of Engineering, professor of aerospace engineering, professor of mechanical engineering, professor of nuclear engineering and radiological sciences in the College of Engineering.