AI model training and inference are driving rapidly rising electricity demand in data centers. Traditional device design, governed by Ohm’s and Fourier’s laws, is reaching limits for reducing dissipation. New multiscale simulation approaches are pointing the way to fundamentally lower-energy hardware.
The Energy Equation: AI’s Growing Footprint
Large-scale AI workloads consume vast amounts of power for compute and cooling. Conventional electronics design relies on diffusive transport models where electrons and heat spread according to simple conductivity and thermal diffusion laws. Those assumptions constrain how much further energy per computation can be lowered without changing the underlying physics of transport.
Quantum Hydrodynamics: A Fluidic Path to Efficiency
In certain high-purity or cryogenic materials, electrons and heat no longer behave like independent diffusing particles. Experiments reveal coherent waves, vortices, and viscous flow where carriers interact collectively. This fluid-like behavior offers pathways to suppress scattering and reduce entropy production per operation.
Multiscale simulation frameworks developed at Columbia University bridge quantum transport at the nanoscale with continuum hydrodynamic models at device scales. These frameworks translate quantum scattering and carrier correlations into effective viscous and coupled electron-phonon transport equations. Tools such as COMSOL Multiphysics are used to implement and solve viscous heat equations and coupled multiphysics problems, enabling predictive modeling of nondiffusive regimes and performance tradeoffs.
Designing Tomorrow’s Devices Today
By capturing nondiffusive transport, engineers can simulate low-dissipation device geometries and materials before fabrication. That means designing transistors, interconnects, and thermal management systems that exploit hydrodynamic flow to reduce Joule heating and thermal bottlenecks. The result could be processors and accelerators that perform more AI work per watt.
Conclusion
Multiscale quantum-hydrodynamic simulation connects fundamental physics to practical device design. As AI energy demand grows, these methods offer a clear research route toward electronics that compute with far lower dissipation, accelerating the transition to sustainable, high-performance AI infrastructure.
