Anima’s Formula: How AI Learns the Laws of Physics
Anima’s Formula | AI Learns the Laws of Physics and Simulates Reality. Discover how AI learns the laws of physics using neural operators. Explore how Anima Anandkumar’s work is transforming climate modeling, fusion, and more.
We’ve seen AI write poems, paint portraits, and complete sentences. But a deeper revolution is quietly unfolding: AI learns the laws of physics — and it’s doing so faster than humans ever could. Pioneered by Anima Anandkumar, neural operators are now enabling machines to simulate complex scientific systems — from atmospheric patterns to nuclear fusion — in real time.
Discover how neural operators, pioneered by Anima Anandkumar, are revolutionizing science by enabling AI to simulate complex physical systems — from weather to nuclear fusion — in seconds.
👩🔬 Who Is Anima Anandkumar?
Anima Anandkumar is a leading researcher in artificial intelligence, currently serving as the Bren Professor of Computing and Mathematical Sciences at Caltech. Born in Mysore, India, she received her PhD from Cornell University and held positions at MIT, UC Irvine, and Amazon Web Services. She also served as Director of ML Research at NVIDIA and is now recognized as one of the most influential voices at the intersection of AI and science.
🏆 Awards and Recognition
She was recently named in the 2025 TIME100 list of most impactful figures in AI, praised for her efforts to accelerate scientific discovery. Anandkumar is also a recipient of the Blavatnik Award, the IEEE Kiyo Tomiyasu Award, and is a fellow of ACM and AAAI.
🧠 The Scientist Behind Neural Operators
Anandkumar is best known for pioneering neural operators—AI models that can learn and simulate physical laws directly from data. These models enable real-time simulations of complex systems like climate dynamics, nuclear fusion, and biomedical devices. Neural operators are not just improving predictions; they’re replacing traditional equations with data-driven physics.
📆 Research Timeline: Neural Operators by Anandkumar’s Team
- 2019–2020: Anandkumar and her collaborators introduced the Fourier Neural Operator (FNO), a deep learning framework designed to learn solutions to partial differential equations (PDEs).
→ Source: NeurIPS 2020 paper by Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli & Anima Anandkumar. - 2021: The model gained attention in climate science and fluid dynamics for enabling real-time predictions from sparse data.
- 2022–2023: Further developments were made in collaboration with NVIDIA and Caltech, focusing on fast, scalable AI models for physical simulations.
- 2024–2025: Anandkumar’s contributions were widely recognized. She was named to TIME100 AI in 2025 for accelerating science with AI-powered physics modeling.
🌐 Why It Matters
Her vision is reshaping how we approach scientific modeling. With neural operators, AI isn’t just analyzing data — it’s learning how the universe works. This has opened new frontiers in:
- Real-time weather forecasting
- Simulating plasma behavior in fusion reactors
- Designing next-gen materials and medical systems
Anima’s Formula: How AI Learns the Laws of Physics
Anima Anandkumar is Accelerating Scientific Discovery with AI
For years, we’ve watched artificial intelligence write poetry, mimic voices, and even generate lifelike images with startling ease. But beneath the creative surface of AI lies a much more profound and disruptive potential — the ability to simulate the physical world.
Enter Anima Anandkumar, a trailblazing researcher at Caltech, and her groundbreaking work on neural operators. Unlike traditional AI models that predict trends or generate content, neural operators learn the underlying equations of nature — unlocking simulations of climate systems, nuclear fusion, fluid dynamics, and biomedical processes in mere seconds.
The implications are staggering:
What if AI could predict the weather faster than our most advanced supercomputers?
What if it could simulate new materials or drug reactions before we ever run a lab test?
What if the next Newton wasn’t human at all — but machine?
This is not science fiction. It’s happening now.
🧩 What Are Neural Operators?
Traditional AI models — like the ones used in chatbots or image generators — are trained to map inputs to outputs. Show them enough cat photos, and they’ll generate one. Feed them millions of sentences, and they’ll finish yours. But when it comes to solving physical problems, like how fluids move or how heat spreads, these models fall short.
That’s where neural operators come in.
Instead of learning from raw data alone, neural operators are designed to learn mathematical functions — the kind that govern PDEs (partial differential equations) found in physics, climate science, and engineering. Think of them as AI systems that don’t just guess based on patterns… they learn the rules of the universe.
🌍 Real Example: Simulating Weather in Seconds
A typical weather model requires a massive supercomputer and hours of calculation. But with models like FourCastNet, a neural operator developed at Caltech and NVIDIA, a 7-day weather forecast can be generated in under 2 seconds.
It doesn’t just estimate — it simulates the atmosphere’s behavior based on physical laws, not just historical trends.
⚛️ Why It Matters
- Speed: Neural operators are hundreds to millions of times faster than traditional solvers.
- Versatility: Once trained, they can be applied to multiple systems — from modeling hurricanes to plasma in nuclear fusion.
- Scalability: They can simulate at extreme resolutions without the traditional computational cost.
So, in a world where data is abundant but scientific computation remains painfully slow, neural operators might be the key to unlocking real-time discovery across every scientific field — from climate to quantum physics.
🔮 From Forecasting to Fusion
The true power of neural operators isn’t just theoretical — it’s already reshaping fields once thought untouchable by machine learning.
☁️ Weather Forecasting: FourCastNet
Developed by Caltech and NVIDIA, FourCastNet is one of the most advanced neural operator-based climate models to date. Instead of crunching numbers for hours like traditional weather simulations, FourCastNet learns the governing dynamics of the atmosphere and outputs a 7-day global forecast in under 2 seconds.
- 🌀 It can track hurricanes, jet streams, and even El Niño patterns in real time.
- 💾 It uses 100 terabytes of historical climate data — but delivers results faster than any supercomputer.
- 🌍 Its low-energy cost and lightning speed could revolutionize climate adaptation strategies worldwide.
⚛️ Nuclear Fusion: Modeling the Unpredictable
Nuclear fusion is one of science’s most complex frontiers — where chaotic plasma reactions behave in ways that defy standard models. Yet neural operators, trained on physics-based simulations, are starting to predict plasma behavior in fusion reactors like ITER and SPARC with astonishing accuracy.
This could:
- Help engineers design better reactors,
- Reduce experimental failures,
- And accelerate the path to clean, limitless energy.
🧬 Biomedicine and Material Discovery
In the biomedical space, neural operators are being trained to simulate:
- Blood flow in arteries,
- Protein folding,
- And new material properties at atomic scale — areas where trial-and-error once dominated.
By learning the equations behind biology and chemistry, AI models could one day design personalized drugs or artificial organs long before human trials even begin.
🧠 In Summary
Whether it’s atmospheres, atoms, or arteries, neural operators mark a new era where AI doesn’t just analyze the world — it actively models it. Fast. Cheap. Scalable.
It’s no longer about asking AI for answers.
It’s about giving it the laws of nature — and watching what it builds.
🔍 The Future of Science Without Equations?
For centuries, scientific discovery has followed a familiar path:
Observe → Hypothesize → Model → Test → Repeat.
But what happens when artificial intelligence skips half those steps?
What if it can predict the behavior of nature — without understanding it in human terms at all?
🧮 The End of Human-Derived Models?
Neural operators don’t rely on symbolic equations. They don’t need to “know” Newton’s laws or Maxwell’s equations. Instead, they absorb patterns from massive amounts of data and produce results that often match or even surpass traditional physics-based models.
This leads to a provocative question:
Do we still need to “understand” nature to model it accurately?
What if the next big breakthrough doesn’t come from theory — but from training?
🧠 Knowledge vs. Comprehension
A neural operator may simulate airflow over a wing more precisely than any human model — but it can’t tell you why.
This raises uncomfortable possibilities:
- Will science become black-box experimentation, where we trust outputs we can’t explain?
- Will we value results over reasoning?
- Can we even call it science if there’s no testable hypothesis — only accurate predictions?
🌐 A New Scientific Paradigm?
Some researchers see this as a renaissance:
AI-driven modeling could free us from centuries of limited symbolic math, opening doors to patterns and systems too complex for the human mind.
Others warn of a dangerous overreliance:
In a world where the why is unknowable, the consequences of blind trust in machine predictions could be catastrophic.
💭 Perhaps the deeper shift isn’t in science itself —
but in what we accept as “understanding.”
💡 Final Thought:
What if the next great scientific mind doesn’t publish papers — but parses petabytes?
As neural operators quietly reshape physics, climate science, and medicine, we stand at the edge of a new epistemological era — one where discovery may no longer begin with curiosity, but with computation.
At deepnods, we ask not just what the future can do — but what it means.
📚 Sources & Further Reading
- TIME Magazine – Anima Anandkumar and the Rise of Neural Operators
- Caltech Newsroom – “Physics-Informed Deep Learning at Scale”
- Nature Machine Intelligence – Learning Operators with DeepONets
- NVIDIA Blog – FourCastNet: AI for Real-Time Climate Forecasting
- MIT Technology Review – AI that Learns the Laws of Nature
