Plenary Talk

By looking back at the technology sector, common phrases such as the Internet of Things, Industry 4.0, Big Data, and Artificial Intelligence dominated the discussions through various fields of research, industry, and economy. Due to the rapid development of parallel processing hardware technology, algorithms, and the corresponding software solutions since 2015, this trend experienced unprecedented acceleration, with the release of the first ChatGPT version in late 2022, highlighting the age of AI. Despite the ongoing development of Artificial Intelligence, the excessive expectations in the technology followed by depression and disappointment as one part of a typical hype cycle, this technology offers a wide range of possibilities for researchers and engineers in the field of theoretical, experimental, and computational vibroacoustics if applied correctly and with care. This presentation examines current developments in artificial intelligence together with risks and opportunities when utilizing the technology to solve problems in the field of vibroacoustic. The importance of high-quality data and its associated nature are stressed, leading to a knowledge-and-experience-enhanced artificial intelligence (keeAI) incorporating problem-specific domain expertise of the developer and engineer, respectively. The presented examples serve as a possible template for future developments and help to facilitate the application of Big Data approaches and Artificial Intelligence beyond the disappointment of excessive expectations.

Doctor Maeder is a fully employed Research Associate at the chair of Vibroacoustics of Vehicles and Machines at the TUM School of Engineering and Design at the Technical University of Munich (TUM), Germany, from which he also received his doctoral degree in Engineering Mechanics with his work “Sound and vibration in a mixed frame - Applications in aeroacoustics and rotor dynamics”. Doctor Maeder is the author and co-author of numerous peer-reviewed journal articles, a book chapter, a patent, and over 50 conference contributions on various research topics in numerical and experimental vibroacoustics. Specifically, this includes applications in the automotive and aerospace sector, civil engineering, medical equipment, and material property identification of monolithic and carbon composite structures. His research is based on the pillars of fundamental theory, numerical methods, experiments, and data-driven approaches to understand wave propagation in solids and fluids and their interaction. In addition to his research, doctor Maeder is teaching courses in “Machine Learning” and “Experimental Vibroacoustics” at TUM and was a guest lecturer for “Noise, Vibration, and Harshness” at Tongji University, Shanghai, China. Besides his commitment as a reviewer for various international journals, Marcus Maeder is an Associated Editor for Numerical and Computational Acoustics for Acta Acustica and the current chair of the Technical Committee on Computational Acoustics within the European Acoustics Association.

In this talk, I will present our recent advances in applying machine learning to underwater acoustic source localization and propagation modeling. Our studies on source localization cover a range of environments, from shallow waters to deep sea scenarios, utilizing both real and synthetic data sets for training. The results demonstrate the superiority of machine learning methods over traditional approaches in handling the environmental uncertainties. We will also discuss some conclusions and the challenging problems encountered in source localization applications. The second focus of this presentation is on acoustic propagation modeling using neural operators. Unlike physics-informed neural network (PINN) methods, neural operators derive underlying relationships primarily from extensive, well-prepared data sets. Instead of learning mappings between finite-dimensional Euclidean spaces, these data-driven neural operators learn mappings between infinite-dimensional function spaces, which is particularly attractive for sound propagation modeling tasks. We will examine the generalization capabilities of neural operators when applied to sound propagation modeling in range-independent shallow water environments.

Dr. Haiqiang Niu is a full Professor at the State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences. He serves as an associate editor for the Journal of the Acoustical Society of America (JASA) and as a guest editor for the Journal of Marine Science and Engineering (JMSE). He is also a member of the Young Scientist Committee for several journals, including Chinese Physics Letters, Chinese Physics B, Acta Physica Sinica, Physics, and Acta Acustica. Additionally, he is a member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences. Dr. Niu received his Ph.D. in Acoustics from the Institute of Acoustics, Chinese Academy of Sciences, in 2014. From 2015 to 2017, he worked as a postdoctoral researcher at the Scripps Institution of Oceanography, University of California San Diego. He became an associate professor in 2018 and was promoted to full professor in 2021. His research interests include machine learning in ocean acoustics, sparse Bayesian learning in acoustical signal processing, geoacoustic inversion, and ocean acoustical tomography. One of his papers on machine learning has garnered over 200 citations. Dr. Niu has been invited to present at several international conferences.

In this talk, recent advances in acoustic and elastic meta-structures to control noise and vibration are presented aiming at practical applications to real-world problems in our daily life and various industries. The first example is an ultra-light (20 times lighter than existing materials) soundproofing meta-panel to insulate broadband noise generated from electric vehicles, with the aid of negative mass density in low-frequency range (road noise) and negative bulk modulus in high-frequency range (motor noise). The second one is an acoustic meta-liner to insulate noise in a duct while allowing flow, by designing ultra-thin acoustic meta-surface with the consideration of visco-thermal losses in deep-subwavelength-scale Helmholtz resonators. The third one is a meta-damper to suppress vibration in beams or plates using waveguide absorbers based on Archimedean spiral acoustic black holes. For the three meta-structures, we present how to design them theoretically and validate their performance experimentally with a couple of audiovisual demonstrations.

Wonju Jeon is an Associate Professor in the Department of Mechanical Engineering at Korea Advanced Institute of Science and Technology (KAIST) and leading the Wave Energy Control Laboratory. Before joining KAIST in 2014, he worked for National Institute for Mathematical Sciences to bridge the gap between applied mathematics and engineering sciences for 7 years. He received his Ph.D. from KAIST in 2006, with a focus on mathematical theory of acoustic diffraction aiming at reducing fan noise. He aims to develop new and innovative solutions for long-standing problems of sound and vibration occurring in various industries such as home appliances and future mobilities. To overcome the limitation of existing materials or technologies, he proposed new concepts of meta-structures (‘meta’ in Greek means ‘beyond’ in English) such as ‘spiral acoustic black holes’, ‘complex-valued impedance tiles’, ‘ultra-light soundproofing meta-panels’ and ‘sound-absorbing metasurface'. He published papers in the field of sound and vibration, pure and applied mathematics, theoretical biology and applied physics. He gave keynote lectures at The 49th INTER-NOISE (2020) and The 23rd Workshop of the Aeroacoustics Specialists Committee of the Council of European Aerospace Societies (2019). Currently, he serves as Secretary General of The 31st International Congress on Sound and Vibration (2025) and Technical Program Committee of Phononics 2025.

The Perfectly Matched Layer (PML) technique has become a reliable and efficient method for computing free-field numerical approximations in time-domain and frequency-domain models over the last three decades. Its combination with the widely used finite difference and finite element methods has spread its popularity and generalized its use to broad different areas of applications such as outdoor acoustics, aeroacoustics, structural mechanics, underwater acoustics, electromagnetism, optics, or geophysics, all of them with a common denominator: the wave propagation phenomena settled initially in an unbounded physical domain of interest. Currently, the PML technique is a key component in many well-known, established commercial and open-source software packages in computational acoustics. The study of its robustness, usability, and accuracy has gained attention from various research communities, facilitating the numerical analysis of its properties, extending the PML techniques to a number of different models, and enhancing its computational performance with optimal settings. This talk reviews the theoretical properties and computational capabilities of different state-of-the-art PML methods, and discusses the open questions and challenges that will be addressed in the near future of PML technique development.

A. Prieto is an associate professor at the Department of Mathematics, University of A Coruña (Spain), and an associate researcher at the Galician Center of Mathematical Research and Technology (CITMAga). His research lines focus on developing and analyzing novel numerical methods and applying mathematical modelling strategies to compute and solve challenging problems in computational acoustics and structural mechanics, especially in complex systems in natural environments (such as coastal underwater regions) or equipment in industrial facilities. His recent work has been pioneering in the application of non-parametric data-driven mathematical techniques to the classification of the nature of seabed in coastal areas, the numerical characterization of porous absorbing materials for room acoustics, and underwater cloaking purposes. Over the last few years, he has pursued various research studies, extending the classical partition of unity finite element method to heterogeneous materials, and improving the robustness of the Perfectly Matched Layer technique by using optimal settings with minimal additional computational effort. He has been the PI of different European and national research projects, one of which was recently endorsed by UNESCO as one of the projects of the 2021-2030 United Nations Decade of Ocean Science for Sustainable Development.

Taiwan has set an ambitious target of achieving carbon neutrality by 2050, with offshore wind power anticipated to generate 40–55 GW. The rapid development of offshore wind farms along the Eastern Taiwan Strait (ETS) introduces significant underwater noise from construction activities such as pile driving, increased vessel traffic during surveys and construction, and continuous operational noise over a projected lifespan of 20–30 years. This escalating anthropogenic noise imposes considerable stress on the marine soundscape and its ecological constituents. Our research focuses on long-term acoustic monitoring at wind farm sites (2014-2024), investigating the changes in sound levels and their ecological consequences, particularly the acoustic behaviors of fish and the critically endangered Taiwanese humpback dolphin (Sousa chinensis taiwanensis). For the first time, our findings reveal clear impacts of pile-driving and operational noise on fish vocalization patterns and demonstrate how vessel traffic and windfarm-related noise influence the acoustic behavior of Taiwanese humpback dolphins. Additionally, we have developed automated detection algorithms to effectively identify vocalizations of both fish and dolphins from extensive acoustic datasets. As offshore wind energy expands, these long-term acoustical datasets serve as essential baseline data for monitoring evolving marine soundscapes and understanding the acoustic responses of marine species throughout the lifecycle of offshore windfarms. These insights are critical for informing conservation strategies and sustainable management practices in Taiwan’s transition to renewable energy. This research is funded by Taiwan National Science and Technology Council, Unitech Inc., Ørsted Taiwan, Copenhagen Infrastructure Partners (CIP), Taiwan Power Company.

Dr. Chi-Fang Chen received her Ph.D. in the Department of Ocean Engineering, Massachusetts Institute of Technology in 1991, and started her career as the faculty member of the Department of Naval Architecture of National Taiwan University from 1991 till now. (Department of Naval Architecture was renamed as Department of Engineering Science and Ocean Engineering in 2000). Her research expertise and interests are underwater acoustics and underwater acoustic propagation. She conducts passive acoustic monitoring (PAM) in recognizing sounds from different species in the ocean which includes Sousa chinensis- in Taiwan waters. Her goal in the near future is to establish a PAM network in coastal waters of western Taiwan to monitor the endangered species, namely the Chinese White Dolphin (Sousa chinensis), and other marine lives. The PAM network is a major component in the integrated dolphin monitoring network, and is composed of fixed stations (bottom-mounted, moored hydrophones, or sonobuoys) and mobile platforms with acoustic payloads such as ASVs.

This paper presents a comprehensive review of key developments in four major branches of acoustics—Underwater Acoustics, Structural Acoustics, Computational Acoustics, and Aeroacoustics—as reflected in contributions published in the Journal of Theoretical and Computational Acoustics over the past thirty years. For each of these areas, we systematically classify the literature into three major categories:

1. Review Articles that synthesize foundational knowledge and provide historical context,
2. New Methodologies that introduce cutting-edge theoretical frameworks and advanced computational techniques, and
3. Emerging Applications that showcase the practical deployment of recent innovations, often validated through experimental investigations.

This structured review highlights major trends, breakthroughs, and evolving research directions in both theory and application. In Underwater Acoustics, advancements in signal propagation modeling and sonar system design are emphasized. Structural Acoustics features innovations in wave-structure interaction and vibroacoustic coupling. Computational Acoustics showcases high-performance numerical solvers, boundary element and finite element methods, and hybrid algorithms. In Aeroacoustics, recent progress includes improved modeling of turbulent flows, noise prediction for aircraft and wind turbines, and experimental validation of simulation results. By curating and contextualizing this body of work, we aim to provide researchers, educators, and practitioners with a valuable reference point and roadmap for future research. We hope this review serves as a gateway to deeper exploration, enabling readers to trace developments through the referenced literature and engage with ongoing innovations in these dynamic fields of acoustics.

Dr. Sean F. Wu received his B.S. in Mechanical Engineering from Zhejiang University (China), and both his M.S. and Ph.D. in Mechanical Engineering from the Georgia Institute of Technology, USA. He began his academic career at Wayne State University (WSU) in 1988 as an Assistant Professor for Research in the Department of Mechanical Engineering. He transitioned to a tenure-track position in 1990, was promoted to Associate Professor in 1995, and to full Professor in 1999. In recognition of his distinguished contributions, Dr. Wu was unanimously named the Charles DeVlieg Professor of Mechanical Engineering (2002–2005) and was appointed University Distinguished Professor by the WSU Board of Governors in 2005—a title he has held continuously since. Dr. Wu is a Fellow of both the Acoustical Society of America (ASA) and the American Society of Mechanical Engineers (ASME). He currently serves as Associate Editor of the Journal of the Acoustical Society of America (JASA), and as Managing Editor and Co-Editor-in-Chief of the Journal of Theoretical and Computational Acoustics (JTCA). Dr. Wu has authored more than 70 peer-reviewed journal articles and holds over 20 U.S. and international patents. He is the author of the textbook The Helmholtz Equation Least Squares Method for Acoustic Radiation and Reconstruction (Springer, 2015), and Co-Editor of the four-volume series Advances in Underwater Acoustics, Structural Acoustics, and Computational Methodologies (World Scientific Publishing, 2025):

• Volume 1: Advances in Underwater Acoustics
• Volume 2: Advances in Structural Acoustics
• Volume 3: Advances in Computational Acoustics
• Volume 4: Advances in Aeroacoustics

Throughout his career, Dr. Wu has mentored more than 40 graduate students (Ph.D. and M.S.), whose work has garnered 24 Best Student Paper Awards at major professional conferences. His honors and awards include:

• Per Bruel Gold Medal for Noise Control and Acoustics, ASME (2018) – the highest ASME honor in acoustics, and the first Asian recipient since the award’s inception in 1987
• Second Prize, Ignite Innovation Competition (2018), State of Michigan – for the invention Cardio Sound Blood Pressure Meter
• Zhu Kezhen Overseas Distinguished Alumni Award, Zhejiang University (2011)
• Outstanding Faculty Service Award (2011) and Advisory Board Service Award (2010), Wayne State University
• Inventor of the Year Award, Wayne State University (2006, 2003)
• Alpha Award for Technology Innovation, Engineering Society of Detroit (2006)
• Inductions into the Asian Academy Hall of Fame and Academy of Asian Leaders (2004)
• Best Small Business Technology Award, State of Michigan (2004)
• Outstanding Graduate Mentor Award, WSU (2004)/li>