Chemical engineering researchers earn first publication for Oklahoma in top AI conference

Thursday, February 19, 2026

Media Contact: Desa James | Communications Coordinator | 405-744-2669 | [email protected]

Dr. Zeyuan Song, a recent Ph.D. graduate of the School of Chemical Engineering at Oklahoma State University, and Dr. Zheyu Jiang, assistant professor for CHE, have achieved a milestone rarely seen in Oklahoma's research landscape: acceptance into the International Conference on Learning Representations 2026, one of the world's most competitive and influential academic conferences in artificial intelligence and machine learning. 

ICLR ranks among the top AI venues globally - second in the field by h-index - and is known for debuting many of the breakthroughs that have shaped modern AI, including the variational autoencoder and the graph attention network. Each submission undergoes a monthslong, double-blind review and rebuttal process, making acceptance highly selective. 

"I am proud of the research excellence Zeyuan achieved during his Ph.D. study in my research lab," Jinag said. "I have been impressed by his ability to bring in new ideas from diverse fields in mathematics, engineering, and AI. This, when combined with a deep understanding of the cutting-edge breakthroughs in the field, leads to this outstanding work published in ICLR." 

Song's paper, titled Adaptive Fourier Mamba Operators, introduces a powerful new machine learning framework for modeling complex natural and engineering phenomena described by partial differential equations.  

"Imagine you are baking a cake," Jiang said. "The temperature of the cake isn't determined by time alone. The outside heats faster than the inside, and the top browns more quickly than the bottom. Partial differential equations describe changes that happen simultaneously in space and time, like how heat moves through a cake as it bakes." 

These types of equations govern real-world phenomena such as fluid dynamics, heat transfer, quantum mechanics  and even the financial market. 

Unlike traditional numerical solvers, which can become extremely time-consuming to solve, Song's AFMO method uses a mathematically grounded neural operator framework to learn how these systems behave, often with greater efficiency and generalizability. 

According to the paper, AFMO integrates two computational frameworks, Adaptive Fourier decomposition, a novel signal processing technique that builds orthogonal spectral bases tailored to the problem, and state-space models, an emerging neural network architecture that can efficiently handle long-range dependencies, to solve general nonlinear partial differential equations. 

"Imagine you are playing piano," Jiang said. "Standard Fourier neural operator plays every song on a standard piano. The piano keys are fixed, and you play by mixing those fixed notes. It works great when the song fits that instrument well, but it can struggle if the 'song' has unusual rhythms. Adaptive Fourier decomposition, on the other hand, is like a custom keyboard tailored to the particular song one wants to play.

"Meanwhile, a state-space model is like a super-fast musician who reads the music left-to-right and keeps a small memory of what happened so far, so they can play very long songs efficiently. Therefore, AFMO builds a custom instrument for each song first, and then has the super-fast musician to play it, so it has the right instrument and efficient playing." 

By uniting these in a novel way, AFMO can solve PDEs on irregular shapes and complex geometries, capture sharp features and singularities, and produce results that are both highly accurate and computationally efficient.

"These are especially challenging problems to solve due to the intricacies of the systems involved," Jiang said. "They require us to think out of the box and develop truly innovative solutions."

In extensive testing, the method consistently outperformed leading neural operator models across diverse benchmark problems, ranging from modeling fluid flow in airfoils and pipes to predicting European option prices in financial mathematics.  

Song's accomplishment represents more than an individual's success. 

This publication is the first ICLR paper from the state of Oklahoma. Notably, this work comes from a chemical engineering department, rather than a traditional computer science or electrical engineering program. 

"As AI continuously transforms the world, we are in an exciting era for interdisciplinary research," Jiang said. "We are thrilled to see the broader impacts and implications of this work in helping OSU recruit talented students, forming cross-department collaborations, and competing for more federal and industry funding to support AI for Science research that pushes toward AI capacity and workforce development in Oklahoma."