The importance of tipping points has been extensively studied, including in a recent @UN @ScienceBoard_UN brief for climate modeling. But it is challenging to detect tipping points before they happen since they can appear suddenly. We address this in our latest paper appearing in @PNASNews Our recurrent neural operator tracks the history of changes and can predict the probability of tipping points happening in future and when it is likely to occur. The RNO was able to look more than two years ahead of the expected tipping point in the simulated data and predict its timing with an error of about 11 days. The RNO was also run on simulations of airflow over a tilting plane wing and it succeeded in predicting the tipping point, suggesting it can potentially help with future aeronautical and aerospace designs. @Caltech news: caltech.edu/about/news/forec… @PNASNews paper: pnas.org/doi/10.1073/pnas.26… @ScienceBoard_UN brief: un.org/scientific-advisory-b…

Great to have participated in preparing the @ScienceBoard_UN brief on tipping points with clear definitions and also on how AI is impacting the field. Our paper on using Neural Operators for early detection of tipping points by measuring deviation against baseline physics is helpful in a number of areas: from climate tipping point in stratocumulus cloud cover to airfoil wake and stall transitions using only limited knowledge of the governing equations. arxiv.org/abs/2308.08794 @mliuschi @Caltech

Neural operators – Convert popular neural networks into neural operators for scientific modeling Extending neural networks to function spaces: While many phenomena are inherently described by functions, neural networks define vector-to-vector mappings that rely on fixed discretizations of the input and output. Neural operators instead define learnable function-to-function mappings that guarantee consistent predictions across different discretizations of the input and output functions. By respecting the functional nature of the data, neural operators can achieve improved performance and generalization. Translating the success of deep learning to operator learning: Careful engineering of neural architectures has been a key factor in deep learning’s success. Translating these architectures to neural operators is crucial for operator learning to enjoy the same empirical optimizations. Our new intro to neural operators (arxiv.org/abs/2506.10973) is officially published in @NatMachIntell It includes: Key principles for constructing NOs Recipes for converting popular architectures (CNNs, GNNs, transformers, etc.) into NOs Guidance for practitioners Joint work w/ @julberner @mliuschi @JeanKossaifi Valentin Duruisseaux Boris Bonev @Azizzadenesheli @Caltech

Neural Operators – Deep learning at any resolution Extending neural networks to function spaces: While many phenomena are inherently described by functions, neural networks define vector-to-vector mappings that rely on fixed discretization of the input and output. Neural Operators instead define learnable function-to-function mappings that guarantee consistent predictions across different discretization of the input and output functions. By respecting the functional nature of the data, neural operators can achieve improved performance and generalization. Translating the success of deep learning to operator learning: Careful engineering of neural architectures has been a key factor in deep learning’s success. Translating these architectures to neural operators is crucial for operator learning to enjoy the same empirical optimizations. Key principles for constructing Neural Operators: *Recipes for converting popular architectures (CNNs, GNNs, transformers, etc.) into Neural Operators *Guidance for practitioners arxiv.org/abs/2506.10973 github.com/neuraloperator @julberner @mliuschi @JeanKossaifi Valentin Duruisseaux Boris Bonev @Azizzadenesheli @caltech

2024 was a pivotal year for AI+Science. Our team made exciting contributions. Here are some highlights: tensorlab.cms.caltech.edu/us… 1. Neural Operators as a unifying AI framework for modeling multi-scale processes. We got to write a perspective article in @NatRevPhys and released an open-source library that has been widely adopted. 2. FourCastNet, our Neural-Operator-based model, featured in the @WHOSTP PCAST report on AI+Science. Its speed enables large ensembles to obtain unprecedented predictions of extreme weather events. 3. Designed a novel medical catheter using Neural Operator-based inverse design. 4. Used Neural Operators with reinforcement learning for turbulence stabilization and wall friction reduction. 5. Many other applications where Neural Operators have shown a big impact: nuclear fusion, computational imaging, Carbon capture and storage etc. 6. Gave a @TEDTalks highlighting AI+Science. 7. Exascale training of genome-scale language models for protein design that was recognized as a finalist for the ACM Gordon-Bell prize. 8. State-of-the-art protein-ligand structure prediction that can handle changing protein conformations that Alphafold and other methods cannot. 9. AI+Math innovations with LLM + Lean for theorem proving. 10. Hardware-efficient large-scale training such as gradient projections and mini-sequence transformers. 11. Many honors such as @iitmadras Distinguished Alumnus award, and @BlavatnikAwards