Dimitris Giovanis

About

Dimitris Giovanis is an assistant research professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with a secondary appointment in the Department of Applied Mathematics and Statistics. His research focuses on the development of advanced computational methodologies and tools at the intersection of probabilistic modeling, data science, and physics-informed machine learning. His work aims to accelerate and optimize simulation and analysis for scientific discovery and data-informed decision making in science and engineering. A central objective of his work is the creation of digital twins—virtual representations that integrate data and physics-based models—for a variety of complex systems. Giovanis' research is applied across diverse domains including materials science, natural hazards, health and biomedicine, aerospace engineering, and astrophysics. His methods support the development of models for structural ceramics, energetic materials, carbon-based composites, and amorphous solids, as well as performance-based earthquake and wind engineering, regional hazard modeling, and post-wildfire debris flow analysis. In health and biomedicine, his work involves traumatic brain injury, digital twins of the human heart, and epidemic modeling. His aerospace research includes structural and aeroelastic systems, while in astrophysics, he contributes to space weather modeling. His research is supported by agencies such as the NSF, DOE, and DARPA, including…

Research topics

• Machine Learning
• Artificial Intelligence
• Computer Science
• Mathematics
• Algorithm
• Programming language
• Statistics
• Applied mathematics
• Theoretical computer science
• Mathematical analysis

Selected publications

• UQpy: A general purpose Python package and development environment for uncertainty quantification

  Journal of Computational Science · 2020 · 88 citations

  DOI

• Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

  Computer Methods in Applied Mechanics and Engineering · 2020 · 55 citations

  1st authorCorresponding
  DOI

• MANIFOLD LEARNING-BASED POLYNOMIAL CHAOS EXPANSIONS FOR HIGH-DIMENSIONAL SURROGATE MODELS

  International Journal for Uncertainty Quantification · 2022 · 39 citations

  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 a set of high-dimensional data representing quantities of interest of the computational or analytical model. For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful…

  DOI

• UQpy v4.1: Uncertainty quantification with Python

  SoftwareX · 2023-10-27 · 18 citations

  articleOpen access

  This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to simplify previous tightly coupled features, and improve its extensibility and modularity. To improve the robustness of UQpy, software engineering best practices were adopted. A new software development workflow significantly improved collaboration betwe…

  PublisherOA PDFDOI

• Accelerating uncertainty quantification in incremental dynamic analysis using dimension reduction-based surrogate modeling

  Bulletin of Earthquake Engineering · 2024-12-11 · 7 citations

  articleOpen access1st authorCorresponding

  We propose a surrogate modeling framework based on dimension reduction to facilitate the quantification of seismic risk of structural systems in performance-based earthquake engineering. The framework adopts incremental dynamic analysis (IDA) for addressing hazard variability, and promotes significant computational efficiency improvement for propagating epistemic uncertainties associated with the structural models. It utilizes both linear and nonlinear dimension reduction approaches, equipped wi…

  PublisherDOI

Frequent coauthors

• Michael D. Shields

  23 shared

• Vissarion Papadopoulos

  National Technical University of Athens

  20 shared

• Dimitrios Loukrezis

  9 shared

• Ioannis G. Kevrekidis

  Johns Hopkins University

  8 shared

• Katiana Kontolati

  Johns Hopkins University

  6 shared

• Manolis Papadrakakis

  National Technical University of Athens

  6 shared

• Paris Papavasileiou

  6 shared

• George Stavroulakis

  National Technical University of Athens

  5 shared

Education

• Phd, Civil Engineering

  National Technical University of Athens

Awards & honors

• DARPA INTACT Grant (2025)
• Richard J. Carroll Memorial Lecture
• Ross B.. Corotis Lecture

Similar researchers at Johns Hopkins University

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• James Guesth-37