A5005508184- Probabilistic and Robust Engineering Design
- Structural Health Monitoring Techniques
- Model Reduction and Neural Networks
- Seismic Performance and Analysis
- Wind and Air Flow Studies
- Advanced Multi-Objective Optimization Algorithms
- Software Engineering Research
- Topology Optimization in Engineering
- Business Process Modeling and Analysis
- Structural Analysis of Composite Materials
- Nonlinear Dynamics and Pattern Formation
- Geotechnical Engineering and Analysis
- Automotive and Human Injury Biomechanics
- Machine Learning in Materials Science
- Ecosystem dynamics and resilience
- Metallic Glasses and Amorphous Alloys
- Statistical Methods and Bayesian Inference
- Fatigue and fracture mechanics
- Simulation Techniques and Applications
- Neural Networks and Applications
- Face and Expression Recognition
- Fault Detection and Control Systems
- Nuclear Engineering Thermal-Hydraulics
- Software Reliability and Analysis Research
- Statistical and Computational Modeling
Johns Hopkins University
2018-2025
State University of Information and Communication Technologies
2021
National Technical University of Athens
2012-2017
In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of set high-dimensional data representing quantities interest computational or analytical model. For purpose, employ Grassmannian diffusion maps, two-step nonlinear dimension reduction technique which allows us reduce dimensionality and meaningful geometric descriptions parsimonious inexpensive...
This paper presents the latest improvements introduced in Version 4 of UQpy, Uncertainty Quantification with Python, library. In version, code was restructured to conform Python coding conventions, refactored simplify previous tightly coupled features, and improve its extensibility modularity. To robustness software engineering best practices were adopted. A new development workflow significantly improved collaboration between team members, continuous integration automated testing ensured...
This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines affinity between points through their representation as low-dimensional subspaces corresponding to on Grassmann manifold. The method is designed for applications, such image recognition and data-based classification of constrained high-dimensional data where each point itself object (i.e., large matrix) can be compactly represented in lower-dimensional subspace....
Traditionally, neural networks have been employed to learn the mapping between finite-dimensional Euclidean spaces. However, recent research has opened up new horizons, focusing on utilization of deep operators capable infinite-dimensional function In this work, we employ two state-of-the-art operators, operator network (DeepONet) and Fourier (FNO) for prediction nonlinear time history response structural systems exposed natural hazards, such as earthquakes wind. Specifically, propose...
ABSTRACT Introduction Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available “average” that employ a single set of geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant shapes exists Army soldiers, evident from Anthropometric Survey Personnel (ANSUR II). The objective this study is to elucidate effects shape on predicted risk...
Abstract Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach combination simplicity wide-ranging predictive power lattice-grain-defect crystalline materials. Working from hypothesis local atomic environments material are constrained by enthalpy minimization to low-dimensional manifold coordinate space, we develop...