Kuang-Hung Liu

Title: Innovations of Kuang-Hung Liu

Introduction

Kuang-Hung Liu is a notable inventor based in Basking Ridge, NJ (US). He has made significant contributions to the field of chemical production processes and geophysical inversion through his innovative patents. With a total of six patents to his name, Liu's work exemplifies the intersection of machine learning and practical applications in industry.

Latest Patents

One of Liu's latest patents is focused on a surrogate model for a chemical production process. This technology involves a machine-learned model that utilizes a collection of inputs and outputs from simulations and actual production data. The surrogate model can estimate outputs based on given inputs, although it faces challenges in ensuring that suggested outputs are feasible in real-world scenarios. Liu's advancements improve upon previous models by constraining outputs to those that are physically possible.

Another significant patent is related to machine learning-augmented geophysical inversion. This method includes obtaining measured data and prior subsurface data to train data and model autoencoders. The process involves forming an augmented forward model and solving an inversion problem iteratively until convergence is achieved. This innovative approach enhances the accuracy of geophysical data interpretation.

Career Highlights

Throughout his career, Kuang-Hung Liu has worked with prominent companies such as ExxonMobil Upstream Research Company and ExxonMobil Technology and Engineering Company. His experience in these organizations has allowed him to apply his innovative ideas in practical settings, contributing to advancements in technology and engineering.

Collaborations

Liu has collaborated with notable colleagues, including Wei D Liu and Huseyin Denli. These partnerships have likely fostered a creative environment that encourages the exchange of ideas and the development of groundbreaking technologies.

Conclusion

Kuang-Hung Liu's contributions to the fields of chemical production and geophysical inversion highlight his innovative spirit and dedication to advancing technology. His patents reflect a commitment to improving processes through machine learning and practical applications.