About

Nikolaos Bouklas is an Associate Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University and serves as the Director of Graduate Studies for Theoretical and Applied Mechanics. He joined the Cornell faculty in January 2018. Prior to his appointment at Cornell, he was a postdoctoral researcher at the Institute of Mechanical Engineering at EPFL in Switzerland and at the Oden Institute at the University of Texas at Austin. He earned his PhD in Engineering Mechanics from the University of Texas at Austin in 2014 and holds a Diploma in Mechanical Engineering from Aristotle University of Thessaloniki, Greece, obtained in 2008. His research focuses on theoretical and computational solid mechanics, aiming to develop advanced frameworks and methods to better understand materials and structures, with particular interest in soft materials, active materials, biomaterials, fracture, instabilities, and multiscale modeling in coupled multi-physical systems. Recently, his lab has been exploring machine learning-enabled constitutive models and solutions of PDEs that combine data with physical principles.

Research topics

• Computer Science
• Artificial Intelligence
• Composite material
• Materials science
• Mathematics
• Medicine
• Computational chemistry
• Applied mathematics
• Biomedical engineering
• Pure mathematics
• Nanotechnology
• Physics
• Algorithm
• Chemistry
• Polymer chemistry
• Geometry
• Statistical physics
• Surgery

Selected publications

• Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger–Rivlin–Ericksen

  Journal of Computing and Information Science in Engineering · 2024 · 13 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Abstract Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generalization performance. In these models, the stress prediction follows from a linear combination of invariant-dependent coefficient functions and known tensor basis generators. However, thus far the formulations have been limited to stress representations based on the classical Finger–Rivlin–Ericksen form, while the performance of alternative representations has yet to be investigated. In this work, we survey a variety of tensor basis neural network models for modeling hyperelastic materials in a finite deformation context, including a number of so far unexplored formulations which use theoretically equivalent invariants and generators to Finger–Rivlin–Ericksen. Furthermore, we compare potential-based and coefficient-based approaches, as well as different calibration techniques. Nine variants are tested against both noisy and noiseless datasets for three different materials. Theoretical and practical insights into the performance of each formulation are given.

  DOI

• Multiscale simulation of spatially correlated microstructure via a latent space representation

  International Journal of Solids and Structures · 2024 · 9 citations

  • Computer Science
  • Statistical physics
  • Materials science
  DOI

• An Adhesive Hydrogel with “Load‐Sharing” Effect as Tissue Bandages for Drug and Cell Delivery

  Advanced Materials · 2020 · 207 citations

  • Materials science
  • Biomedical engineering
  • Nanotechnology

  Hydrogels with adhesive properties have potential for numerous biomedical applications. Here, the design of a novel, intrinsically adhesive hydrogel and its use in developing internal therapeutic bandages is reported. The design involves incorporation of "triple hydrogen bonding clusters" (THBCs) as side groups into the hydrogel matrix. The THBC through a unique "load sharing" effect and an increase in bond density results in strong adhesions of the hydrogel to a range of surfaces, including glass, plastic, wood, poly(tetrafluoroethylene) (PTFE), stainless steel, and biological tissues, even without any chemical reaction. Using the adhesive hydrogel, tissue-adhesive bandages are developed for either targeted and sustained release of chemotherapeutic nanodrug for liver cancer treatment, or anchored delivery of pancreatic islets for a potential type 1 diabetes (T1D) cell replacement therapy. Stable adhesion of the bandage inside the body enables almost complete tumor suppression in an orthotopic liver cancer mouse model and ≈1 month diabetes correction in chemically induced diabetic mice.

  DOI

Recent grants

• A Multiscale Investigation of Fatigue Induced Damage Progression in Tendon

  NSF · $664k · 2021–2025

• GOALI/Collaborative Research: Instabilities and Local Strains in Engineered Cartilage Scaffold

  NSF · $500k · 2022–2025

Frequent coauthors

• Jan N. Fuhg

  The University of Texas at Austin

  37 shared

• Teeratorn Kadeethum

  32 shared

• Mahmut Selman Sakar

  École Polytechnique Fédérale de Lausanne

  19 shared

• Reese E. Jones

  Sandia National Laboratories California

  15 shared

• Hongkyu Yoon

  Sandia National Laboratories

  12 shared

• Fernanda F. Fontenele

  Cornell University

  11 shared

• Ida Ang

  11 shared

• Erik Mailand

  École Polytechnique Fédérale de Lausanne

  11 shared

Awards & honors

• AFOSR Young Investigator Program award 2022
• Presenter Fellowship, U.S. National Committee for Theoretica…
• Greek Diaspora Fellowship Program 2019
• Travel Award from the Center for Mechanics of Solids, Struct…
• Hellenic Professional Society of Texas Scholarship 2014

Similar researchers at Cornell University

• Edward Bucklerh-182
• Robert Sternbergh-168
• John P. Mooreh-153
• Olivier Elementoh-150
• Andy Clarkh-142