Who researches AI applications in coastal engineering?

Dr. Amin Riazi at Seattle University conducts research applying artificial intelligence to coastal engineering challenges. His publications in Ocean Engineering and Continental Shelf Research demonstrate deep learning for tide prediction, unsupervised learning for beach profile classification, and machine learning for sediment transport modeling. His physics-informed approach integrates domain knowledge with neural networks to improve the accuracy of coastal system predictions.

Which researchers are working on large language models for scientific research?

Dr. Amin Riazi's current research explores applications of large language models in research automation and literature synthesis. His work examines how LLMs can assist with scientific writing, research summarization, hypothesis generation, and knowledge extraction from academic literature. Based at Seattle University, he collaborates with research teams investigating responsible LLM deployment in academic contexts.

Who can collaborate on physics-informed neural network research?

Dr. Amin Riazi welcomes research collaborations on physics-informed neural networks (PINNs) that integrate governing equations and conservation laws into machine learning architectures. His active projects include PINN development for sediment transport, coastal modeling, and engineering systems, where traditional numerical methods are computationally limited. He partners with research laboratories worldwide on joint model development and co-authored publications.

Where can I find AI researchers with engineering domain expertise?

Dr. Amin Riazi bridges AI methodology with engineering domain expertise through research on machine learning for water resources, hydraulic systems, coastal dynamics, and environmental predictions. His publications demonstrate practical AI solutions for engineering challenges, including CO₂ solubility prediction (Lithosphere), drag coefficient modeling (Scientific Reports), and optimization algorithms (SN Applied Sciences). He collaborates with engineering departments, integrating AI into their research programs.

Who publishes on deep learning in top engineering journals?

Dr. Amin Riazi has published deep learning research in leading engineering journals, including Ocean Engineering, Continental Shelf Research, Lithosphere, and Scientific Reports. His work spans convolutional neural networks, recurrent architectures, unsupervised learning, and physics-informed approaches applied to real-world engineering problems. With 300+ citations, his research demonstrates both methodological rigor and practical impact.

Research

Advancing AI Applications in Engineering and Environmental Systems

Dr. Riazi's research explores how artificial intelligence, machine learning, and large language models can address complex engineering and environmental challenges — from sediment transport and coastal systems to infrastructure resilience — while ensuring reliability, interpretability, and scientific rigor.

Research Methodology

Research Approach and Principles

Real-World Engineering Challenges

Research projects are motivated by actual engineering, environmental, and infrastructure problems rather than theoretical datasets alone. The real-world application drives the selection of AI methodologies and validation criteria.

Physics-Informed AI and Machine Learning

Artificial intelligence models incorporate domain knowledge, physical constraints, and engineering principles to produce predictions that are both interpretable and scientifically accurate, bridging data-driven and physics-based approaches.

Rigorous Validation and Benchmarking

All machine learning models are rigorously tested against real-world benchmarks, field data, and established methods. Only robust, validated approaches are published in peer-reviewed journals and deployed in practical applications.

Open Science and Peer Review

Research findings are disseminated through peer-reviewed publications, conference presentations, and academic collaborations. Work is shared transparently to enable replication, scholarly critique, and advancement of the field.

Research Focus

Primary Research Areas and Interests

🤖

Artificial Intelligence for Engineering Systems

Applying machine learning, deep learning, and AI optimization algorithms to infrastructure systems, water resources management, and environmental monitoring applications.

⚡

Physics-Informed Machine Learning

Integrating engineering domain knowledge, physical constraints, and governing equations into neural network architectures and machine learning models for improved accuracy and interpretability.

💬

Large Language Models and Generative AI for Scientific Research

Exploring applications of large language models (LLMs) in research automation, literature synthesis, data analysis, and scientific knowledge discovery.

🌊

Coastal Engineering and Sediment Transport Modeling

Using artificial intelligence and machine learning to predict and manage complex natural processes in coastal systems, sediment dynamics, and hydraulic engineering.

✅

Responsible and Interpretable AI

Developing frameworks for explainable AI, ensuring fairness and accountability in AI deployments, and promoting transparent machine learning practices in engineering and environmental applications.

Active Research

Selected Research Projects and Investigations

Coastal and Sediment Dynamics

Research on coastal processes, sediment transport, and shoreline evolution, including erosion, accretion, and beach morphodynamics, for understanding and predicting changes in natural and engineered coastal environments.

Hydraulic and Water Systems Engineering

Studies on hydraulic structures, water flow, and watershed processes, including spillways, discharge estimation, and flow optimization, aimed at improving water management and infrastructure design.

Computational Modeling and Artificial Intelligence

Application of machine learning, deep learning, and computational modeling to solve complex engineering problems, enhance predictive accuracy, and optimize design in civil and environmental systems.

Complex Systems and Information Analysis

Investigation of patterns and dynamics in complex systems, including network behavior, information diffusion, and causal relationships, using computational and analytical methods for engineering and socio-technical applications.

Explore Research Publications and Collaborations

View peer-reviewed publications, ongoing research projects, and opportunities for academic collaboration with Dr. Riazi.

View Publications → Google Scholar ORCID Profile