麻豆淫院

Finding a reasonable hypothesis can pose a challenge when there are thousands of possibilities. This is why Dr. Joseph Sang-II Kwon is trying to make hypotheses in a generalizable and systematic manner.

Kwon, an associate professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University, published his on blending traditional physics-based scientific models with experimental data to accurately predict hypotheses in the journal Nature Chemical Engineering.

Kwon's research extends beyond the realm of traditional chemical engineering. By connecting physical laws with machine learning, his work could impact renewable energy, smart manufacturing, and health care, outlined in his recent paper, "Adding big data into the equation."

Deriving hypotheses for experimental observations typically involves a trial-and-error process. Kwon has developed a systematic framework that integrates specialized knowledge with experimental data to create a more efficient process.

"The most impactful aspect of this research is its ability to bridge the gap between theoretical models and real-world complexity, creating a versatile framework to solve intricate problems," Kwon said. "This versatility means that the potential benefits could reach a wide range of industries and significantly impact daily life."

This research could lead to new drug discovery by incorporating experimental data into these models, Kwon said. This hybrid modeling approach integrates biological knowledge with data to accelerate drug predictions.

"Developing new drugs is expensive and time-consuming," Kwon said. "But with more advanced models, we can accelerate the discovery and manufacturing processes. Leveraging simulations and machine learning reduces the need for costly lab experiments, saving time and speeding up the discovery of new treatments."

His approach combines physics-based models with the flexibility of data-driven components that can adapt and correct predictions based on real-world experimental data.

Kwon plans to use these models as a backbone to simulate complex systems and capture underlying physical phenomena that traditional physics-based models cannot capture alone.

"This methodology allows us to continuously estimate process parameters alongside the hyperparameters of the data-driven component," Kwon said. "By doing so, we ensure that the models are applicable to a wider range of conditions, making them more versatile and better suited to handle new and varied scenarios."

"Purely data-driven models fall short when it comes to capturing the intricacies of these systems," Kwon said. "By blending the two approaches, we can improve the efficiency and reliability of industrial processes that are critical to producing everyday essentials such as energy, chemicals, and pharmaceuticals."