ICML2024 Tutorial on "Neural Operators & Machine Learning on Function Spaces" is now out. #NeuralOperators #AInScience @nvidia @NVIDIAAI youtube.com/watch?v=_j7bceE9… Looking forward to building the future of this field with the bigger community, all together.

A detailed work on how to extend great neural network architectures to neural operator framework, making them readily applicable to operator learning problems. This work has been one of the mind provoking and principle riddle solving works in MLonFunctions.

Reasoning is super intelligence in both forms of intelligence

#NeuralOperators learn physics through data. We study long term prediction capability of #NeuralOperator on a hard task of ocean emulation with variable forcing, making me think very seriously about coupled weather ocean model, #THEModel

FALCON: built on centuries of knowledge from fluid dynamics, turbulent flows, control, RL, and ML, to deliver foundational approach for aviation, tested and trained on real world wing, validated on the real world. This is very exciting, thanks @BBCBreaking for featuring our work.

AI+Science book is now up, After years of work, our book on Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems is available at Foundations and Trends® in Machine Learning. We cover the history of works in the intersection of AI and Science, to novel breakthroughs and advance in foundation of AI. From neural networks to neural operators, From PINNs to PINO and so on, From computer vision to quantum, graph data, grid data, point cloud data, and GenAI in sciences, From classical reduced order methods to learned ones. Etc. With an extraordinary list of co-authors Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, YuQing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, @MinkaiX , Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, @AnimaAnandkumar , @StefanoErmon , Pietro Liò, @yuqirose , Stephan Günnemann, @jure, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, @ShuiwangJi

"NeuralDMD"—a fully interpretable neural framework that fuses neural implicit fields with Dynamic Mode Decomposition to recover spatio-temporal dynamics from very sparse and noisy data. From black-hole imaging to weather nowcasting Jw. Ali SaraerToosi, @tu_renbo, and Aviad Levis @UofT @nvidia Paper: lnkd.in/gtGA7Ywu Website: lnkd.in/gTa5HVTr Code: lnkd.in/gu8FDKuT

Translating the successful techniques, tricks, and developments in neural networks to neural operators to learn on any resolution. We carefully planned out a practical and easy way to translate what we know works in neural networks and bring them all to neural operators, even go beyond and invent more. Paper: arxiv.org/pdf/2506.10973 A joint work with @julberner @mliuschi @JeanKossaifi Valentin Duruisseaux, Boris Bonev, and @AnimaAnandkumar The neural operator library to read through: github.com/neuraloperator

Stable-ChebNet Stable long range dependency is essential for physics, social network, and science, Technically, information needs to traverse effectively through out deep learning models without dissipating by due to lack of design. We propose a fundamentally non-dissipative model with controlled stable information propagation, with strong empirical benefits on all the long range dependency benchmarks. A great work led by @haririAli95, our rising star in ML and graph learning.

We developed the foundations of diffusion model and posterior sampling on function spaces. We carefully extend Tweedie principle to function spaces which sits at the core of our methods. We provide techniques for functional regressions/reconstruction given partial sample observations. This way, we establish a few of the initial steps towards functional regression and data assimilation in science and engineering.

This work has been keeping me awake for quite sometime, The secret sauce behind Adam is its Signum. This is just mind consuming, great work @orvieto_antonio and @gowerrobert. It is also a great news for efficiency and MLSys communities.

Human, mouse, monkey brain imaging, Ultrasound imaging is about the study of wave functions and their functional inversion, constituting a critical path to brain imaging. As a problem on function spaces, we introduce a novel #NeuralOperator technology for imaging, that is 1- exceptionally less invasive, 2- data and energy efficient 3- and fast taking us towards the future of real-time brain imaging.