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Date:  Wed, March 11, 2026
Time:  
Location:  Holmes Hall 244
Speaker:  Dr. Takafumi Ienaga and Dr. Akinori Tomoda, Fukuoka Institute of Technology

Hosted by the UH Manoa College of Engineering

ECE Graduate Students: This will count towards your seminar credit.

Abstract:

Dr. Takafumi Ienaga | My Research Overview: Inclusive Computing Education and Environmental Information Support for Mobile Agents

In this seminar, I will present an overview of the projects I have been involved in, with particular focus on the “Numeric Key Programming Project” and the “R-GIS Project.”

The former focuses on the research and development of computer education for both sighted and visually impaired students. Through this project, we have conducted instructional sessions in real-world classroom settings as well as at various educational events. However, these environments often make it difficult to implement fully controlled experiments, which in turn creates challenges for academic reporting. With this in mind, we would welcome the opportunity to exchange ideas with others who have encountered similar challenges in field-based research.

The latter explores information support systems that provide sensor data and events from smart environments to robots. In this project, we aim to build a system that helps mobile agents—including both robots and humans—navigate and perform tasks even in unfamiliar environments. Since mobile agents differ widely in their locomotion, sensing, and information-processing capabilities, the environment must offer support tailored to each agent’s characteristics. We also aim to ensure that mobility remains robust against malicious environments or adversarial agents. If these topics are of interest, we would greatly appreciate your thoughts and participation in the discussion

Dr. Akinori Tomoda | Physics-Grounded Deep Learning Across Scales: From Neural Network Interatomic Potentials to Interpretable Generative Earthquake Ground Motions and Infrastructure Risk

Deep learning and related data-driven modeling increasingly function as unifying methodologies that integrate physics-based simulation, empirical data, and high-performance computing across various engineering scales. In this seminar, I will introduce two research directions focused on developing physics-grounded, interpretable, and computationally scalable frameworks.

Part 1: I will present our workflow for materials development using neural network interatomic potentials for manganese-based damping alloys, such as the M2052 alloy. The workflow encompasses data generation, potential training, and large-scale molecular dynamics simulations to estimate mechanical properties and damping-related indicators. I will also discuss translating atomistic predictions into continuum constitutive parameters and integrating them into finite-element (FE) structural assessments. This multiscale approach supports the development of new materials and the formulation of design guidelines.

Part 2: This section focuses on the simplified seismic assessment of frictional support systems. I will introduce a friction-system response spectrum and a practical response-estimation framework intended to reduce reliance on extensive nonlinear time-history analyses. The primary model consists of a two-degree-of-freedom (2-DOF) system, with friction at the foundation–support interface and a support–equipment connection represented by a linear spring and a viscous damper. As an outlook on future work, I will describe a direction for graph neural network (GNN)– based generation of spatially correlated multi-point ground-motion fields and their integration with multi-input multi-degree-of-freedom (MDOF) analyses for long structures, such as pipelines, to enable higher-fidelity damage and risk estimation.

I will conclude by highlighting potential collaboration opportunities at University of Hawaii at Manoa in computational materials and machine learning, earthquake engineering, and data-driven simulation frameworks

Biography:

Dr. Takafumi IENAGA is an Associate Professor in the Department of Computer Science and Engineering at the Fukuoka Institute of Technology, where he joined the faculty in 2010. He received the B.E., M.E., and Dr.E. degrees from Kyushu University, Fukuoka, Japan, in 1999, 2001, and 2004, respectively. From 2004 to 2010, he served as a researcher at the Institute of Systems and Information Technology (ISIT), a research institute supported by Fukuoka City. His research interests include human interface, assistive technology, and robotics, with a focus on technologies that support daily human activities. Dr. IENAGA is a member of the Institute of Electrical and Electronics Engineers (IEEE), etc.

Dr. Akinori TOMODA is an Assistant Professor in the Department of Intelligent Mechanical Engineering at the Fukuoka Institute of Technology, where he joined in 2017. He received his B.E., M.E., and Ph.D. degrees from Saitama University, Japan, in 2005, 2007, and 2010, respectively. From 2007 to 2008, he worked as a reactor maintenance engineer at Tokyo Electric Power Company’s Kashiwazaki-Kariwa Nuclear Power Station. From 2010 to 2017, he was an Assistant Professor in the Department of Mechanical Engineering at Saitama University, Japan. His research interests include mechanical vibration, seismic design of industrial facilities, robotics, and the estimation of mechanical properties of metallic materials using first-principles calculations, molecular dynamics simulations, finite-element methods, and deep learning techniques. Dr. Tomoda is a member of the Japan Society of Mechanical Engineers (JSME), etc.