Imagine a world where the design of wind turbines, the aerodynamics of vehicles, the optimization of electronics, the efficient conversion and storage of hydrogen fuel, and the exploration of alternative energy sources took merely a fraction of the time it currently does. Traditionally, these tasks have relied heavily on the creation of mathematical models - a painstakingly meticulous process carried out by human experts - followed by rigorous validation through numerical simulations. This age-old approach, while robust, is increasingly facing its limitations in a rapidly advancing world.

Enter the age of Artificial Intelligence, paving an unprecedented path in the realm of physics. From the seed of an incomplete problem description, AI has the ability to generate a mathematical model of extraordinary accuracy - a model so complex it could confound even the most brilliant of human minds. Our role, as humans, now pivots to becoming the validators of these AI-created models, rigorously testing and scrutinizing them from all perspectives to ensure their validity.

AI's disruptive presence extends further, offering a tremendous advantage when it comes to the utilization of these models in numerical simulations. The computational speed of these AI-generated models outpaces traditional methods by several thousand times, resulting in faster visualizations of physical phenomena for problem-solving, optimization, fault prediction, or technological advancement.

Embracing this accelerated process, we stand on the precipice of a research and development revolution. With the capacity to explore a vastly larger realm of possibilities in the same amount of time as before, we are pushing the boundaries of innovation and discovery. The future is here, and it is powered by AI.

It's essential to see how this revolutionary technology is applied in real-world scenarios. The exciting fusion of AI and physics isn't just theoretical; it's being leveraged right now, disrupting industries and accelerating progress. To truly appreciate the transformative power of AI in this field, let's delve into three compelling use-cases that exemplify this cutting-edge amalgamation at work.

Digital twin of Earth with fast weather prediction

An AI-driven approach was used to model global Earth weather leveraging Adaptive Fourier Neural Operators (AFNO). Team around Pathak et. al. developed FourCastNet (short for Fourier ForeCasting Neural Network), which accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. It has important implications for planning wind energy resources, predicting extreme weather events such as tropical cyclones, extra-tropical cyclones, and atmospheric rivers.

It’s extremely fast, generating a week-long, accurate, 3-dimensional global weather prediction in under 10 seconds, reaching 45,000x speedups over ECMWF Integrated Forecasting System’s (IFS) state-of-the-art Numerical Weather Prediction (NWP) model, with potentially up to 145,000x speedups when dealing with model trained on meteorological dataset of global 30km vertical weather data. Once trained, however, FourCastNet uses 12,000x less energy to generate a forecast than the IFS model. FourCastNet could be trained on currently available GPU hardware in about two months (~1,400 compute hours on a single A100 GPU) with available meteorological dataset from 40 years of global meteorological data collection (ERA5).

Thanks to the speed of such a weather prediction model, it can be used in a wide variety of applications, such as energy and agricultural planning, transportation, and disaster mitigation. Here, we present few animations from visualizing the interactive simulation of FourCastNet model built with NVIDIA Modulus framework and transformed into digital twin of Earth through NVIDIA Omniverse:

Wind farm energy optimization

In a world facing the dire realities of climate change, the quest for cleaner, greener energy is of paramount importance. Siemens Gamesa, a leading name in renewable energy, has taken a giant leap in this direction, harnessing the power of physics-informed neural networks (PINNs) to revolutionize wind energy generation.

Before we delve into their breakthrough, it's important to understand the challenges of wind farm optimization. High-fidelity, transient simulation of a single wind turbine using traditional simulation software with LES turbulence modeling could take an astonishing 30-40 days. Now, consider a wind farm comprising tens, if not hundreds, of such turbines. Examining and reducing possible wake effects in real time, in tandem with fine-tuning wind farms to handle a multitude of wind and weather conditions, necessitates hundreds or even thousands of simulation runs and iterations. Traditionally, this was rendered infeasible due to the formidable time limitations and financial costs involved.

Enter the world of PINNs and AI-assisted positioning. Siemens Gamesa leveraged AI-based physics simulators trained using PINNs, leading to a dramatic acceleration in simulation times. Today, they can simulate tens of wind turbines in mere minutes, unlocking the power of inverse physics simulation for lightning-fast optimization of energy output.

But what does this breakthrough mean for our future? Imagine a world where efficient, green energy generation isn't just a distant ideal, but an everyday reality. The swift simulation and optimization process enabled by PINNs will allow us to rapidly set up and optimize wind farms, harnessing the untapped potential of wind energy at an unprecedented scale and speed.

This is more than just a technological triumph; it's a beacon of hope for a cleaner future. As we march forward, these advancements in AI and physics simulation will play a pivotal role in driving our transition towards sustainable, renewable energy sources.

Blood flow simulation in intracranial aneurysm

Computer simulation is highly beneficial for understanding the physics of blood flows within our bodies, particularly when dealing with dangerous health issues like aneurysms. Thanks to modern technology, we can create 3D geometries from the images of these anomalies using MRI and CT scans. However, such 3D representations vary for each patient, making it quite challenging to simulate blood flow and pressure for every unique case using traditional simulation software. It's a time-consuming process, but in these life-or-death situations, every minute counts. Quick understanding and decision-making can mean the difference between life and death.

With the help of physics-informed neural networks, doctors could analyze each anomaly in a few minutes instead of days or weeks. It’s like having a fast-forward button for crucial medical decisions. They can even explore multiple 'what if' scenarios in a single day, visualizing the potential outcomes of various treatments.

Armed with a comprehensive report of anticipated physiological factors, surgeons can better prepare for complex procedures and help avert disastrous medical events. In essence, these AI-powered tools will provide our medical professionals with a detailed roadmap for each unique patient journey, potentially saving more lives along the way.