- MEEM 829
- Assistant Professor, Mechanical Engineering-Engineering Mechanics
- Faculty member of Center for Data Sciences at the Institute of Computing and Cybersystems (ICC)
- Faculty member of the Center for Applied Mathematics and Statistics
- PhD, Indian Institute of Science (IISc), Bangalore
- MS, Indian Institute of Science (IISc), Bangalore
- BS, Indian Institute of Engineering Science and Technology, Shibpur
Dr. Susanta Ghosh worked as an Associate in Research in the Pratt School of Engineering at the Duke University. He was a postdoctoral scholar at the Departments of Aerospace Engineering and Materials Science & Engineering at the University of Michigan, Ann Arbor. Prior to joining University of Michigan he was a research fellow at the Technical University of Catalunya, Barcelona, Spain. His M.S. and Ph.D. degrees are from the Indian Institute of Science (IISc), Bangalore, and BS degree from Indian Institute of Engineering Science and Technology, Shibpur.
Links of Interest
Postdoctoral Research Experience
- Duke University, Durham
- University of Michigan, Ann Arbor
- Technical University of Catalunya, Barcelona
- Bayesian Machine Learning and Uncertainty Quantification
- Scientific Machine Learning
- Computational Solid Mechanics
- Materials Modeling
- Biomedical Imaging and Inverse Problems
- Pathrudkar, S., Thiagarajan, P., Agarwal, S., Banerjee, A. S., & Ghosh, S. (2023). Electronic Structure Prediction of Multi-million Atom Systems Through Uncertainty Quantification Enabled Transfer Learning. arXiv preprint arXiv:2308.13096. PDF
- P Thiagarajan, S Ghosh, "A Jensen-Shannon Divergence Based Loss Function for Bayesian Neural Networks", arXiv preprint arXiv:2209.11366 PDF
- R. Mattey, B. Jewell, S. Ghosh, T. Sain, "Phase-field fracture coupled elasto-plastic constitutive model for 3D printed thermoplastics and composites", Engineering Fracture Mechanics, Volume 291, 2023, Read more.
- U. Yadav and S. Ghosh, "An atomistic-based finite deformation continuum membrane model for monolayer Transition Metal Dichalcogenides," Journal of the Mechanics and Physics of Solids, 168 (2022) 105033, August 2022. Read More.
- P. Thiagarajan, P. Khairnar and S. Ghosh, "Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2021.3123300. pp. 815 - 825, Volume: 41, Issue: 4, April 2022. Read More.
- R. Mattey and S. Ghosh, "A Novel Sequential Method to Train Physics Informed Neural Networks for Allen Cahn and Cahn Hilliard Equations", Computer Methods in Applied Mechanics and Engineering, Volume 390, 15 February 2022, 114474, Read more.
- U. Yadav, S. Pathrudkar, S. Ghosh, "Interpretable machine learning model for the deformation of multiwalled carbon nanotubes", Physical Review B 103 (3), 035407, 2021. Read more
- Benedict Egboiyi, Revanth Mattey, Shabnam Konica, Parag Nikam, Susanta Ghosh, Trisha Sain, "Mechanistic understanding of the fracture toughening in chemically strengthened glass–experiments and phase-field fracture modeling", International Journal of Solids and Structures, 2021. Read More
- Aowabin Rahman, Prathamesh Deshpande, Matthew S Radue, Gregory M Odegard, S Gowtham, Susanta Ghosh, Ashley D Spear, "A machine learning framework for predicting the shear strength of carbon nanotube-polymer interfaces based on molecular dynamics simulation data", Composites Science and Technology 207, 108627, 2021, Read more
- H Ahmed, S Ghosh, T Sain, S Banerjee. "Hybrid Bessel beam and metamaterial lenses for deep laparoscopic nondestructive evaluation", Journal of Applied Physics 129 (16), 165107, 2021, Read More
- Yadav, U., Coldren, M., Bulusu, P., Sain, T., Ghosh, S., "Interface Fracture of Micro-architectured Glass: Inverse Identification fo Interface Properties and a Novel Analytical Model," Mechanics of Materials, Vol. 137, online July 2019. DOI:https://doi.org/10.1016/j.mechmat.2019.103107 Read More
- S. Ghosh, Olalekan Babaniyi, M. Diaz, Z. Zou, M. Bayat, M. Fatemi and Wilkins Aquino, “Modified Error in Constitutive Equations (MECE) Approach for Ultrasound Elastography,” The Journal of the Acoustical Society of America, Vol. 142, No. 4, Oct 2017. DOI:10.1121/1.5006911 Read More
- S. Ghosh, "A Novel Technique to Obtain Analytical Direct Correlation Functions for use in Classical Density Functional Theory," Computational Materials Science, Volume 138, October 2017, Pages 384-391 Read More
- S. Haldar, T. Sain, S. Ghosh, “A Novel High Symmetry Interlocking Micro-Architecture Design for Polymer Composites with Improved Mechanical Properties,” International Journal of Solids and Structures, Available online 29 June 2017. Read More
- S. Ghosh, Veera Sundararaghavan and A.M Waas. “Construction of Multi-dimensional Isotropic Kernels for Nonlocal Elasticity based on Phonon Dispersion Data,” International Journal of Solids and Structures, 51(2), 2014, pp 392–401. Read More
- S. Ghosh, A. Kumar, A.M Waas and Veera Sundararaghavan. “Non-local Modeling of Epoxy using an Atomistically Informed Kernel,” International Journal of Solids and Structures, 50(19), 2013, pp 2837–2845. Read More
- S. Ghosh and M. Arroyo, “An Atomistic-based 3D Foliation Model for Multilayer Graphene Materials and Nanotubes," Journal of the Mechanics and Physics of Solids. 61, 2013, pp. 235-253. Read More
- H. Shima, S. Ghosh, M. Arroyo, K. Iiboshi and M. Sato, “Thin-shell Theory Based Analysis of Radially Pressurized Multiwall Carbon Nanotubes,” H. Shima, Computational Materials Science, 52(1), 2012, pp. 90-94. Read More
- H. Shima, M. Sato, K. Iiboshi, S. Ghosh and M. Arroyo, “Diverse Corrugation Pattern in Radially Shrinking Carbon Nanotubes,” Physical Review B, 82(8), 085401, 2010. Read More
- S. Ghosh and D. Roy, “An Accurate Numerical Integration Scheme for Finite Rotations using Rotation Vector Parameterization,” Journal of the Franklin Institute, 347, 2010, pp. 1550-1565. Read More
- S. Ghosh and D. Roy, “On the Relation between Rotation Increments in Different Tangent Spaces”, Mechanics Research Communications, 37(6), 2010, pp. 525-530. Read More
- S. Ghosh and D. Roy, “A Frame-invariant Scheme for the Geometrically Exact Beam using Rotation Vector Parameterization,” Computational Mechanics, 44(1), 2009, 103–118. Read More
- S. Ghosh and D. Roy, “Consistent Quaternion Interpolation for Objective Finite Element Approximation of Geometrically Exact Beam,” Computer Methods in Applied Mechanics and Engineering, 198(3-4), 2008, 555–571. Read More
- S. Ghosh and D. Roy, “A Numeric-analytic Form of the Adomian Decomposition Method for Two-point Boundary Value Problems in Nonlinear Mechanics,” Journal of Engineering Mechanics (ASCE), 133(10), 2007, 1124–1133. Read More
- S. Ghosh and D. Roy, “Multi-step Tangential Versus Transversal Linearizations in Nonlinear Dynamics,” International Journal for Numerical Methods in Engineering, 72(5), 2007, 582–605. Read More
- S. Ghosh, A. Roy and D. Roy, “An adaptation of Adomian Decomposition for numeric-analytic integration of strongly nonlinear and chaotic oscillators”, Computer Methods in Applied Mechanics and Engineering, 196 (4-6), 2007, 1133–1153. Read More
- N.G. Stephen and S. Ghosh, “Eigen Analysis and Continuum Modeling of a Curved Repetitive Beam-like Structure,” International Journal of Mechanical Sciences, 47, 2005, 1854–1873. Read More
- R.N. Iyengar and S. Ghosh, “Microzonation of Earthquake Hazard in Greater Delhi Area,” Current Science, 87(9), 2004, 1193–1202.
National Science Foundation, CMMI, MoMS
PI: Susanta Ghosh
Total Awarded Amount: $170,604
Project Period: 2019-2022
“Prediction and Tuning of Spin Selectivity Properties of Chiral Nanomaterials via an integrated Machine Learning - First Principles Approach”.
DOE (Theoretical Condensed Matter Physics).
PI: Amartya Banerjee, Co-PI Susanta Ghosh
Amount: Total: $701,622, Co-PI Ghosh’s Share: $321,941.
Project Period: 2022-2025
Award number: DE-SC0023432
- Bayesian Machine Learning: We develop and analyze Bayesian neural network models primarily to quantify uncertainties in their prediction. We explore various applications of Bayesian Neural Networks such as medical image classification, electronic structure prediction, and material modeling.
- Scientific Machine Learning: We are integrating Machine Learning and Physics-based Computational Science to improve the predictive capabilities of nano-material modeling and various physics problems.
- Computational Inverse Problem: Computational inverse problem for material characterization of soft biological tissues from ultrasound data.
- Crystal-elasticity-based membrane modeling for 2D materials and carbon nanotubes: Development of computationally efficient atomistic-continuum models for Transition Metal Dichalcogenides, multilayer Graphene materials, and Carbon Nanotubes containing hundreds of millions of atoms.
- Phase Filed Crystal (PFC) modeling: PFC modeling to study elastic and inelastic deformation in a wide variety of non-equilibrium phenomena at microscopic length scales and on diffusive time scale.
- Nonlocal elasticity of epoxy with atomistic kernel: Nonlocal elasticity based length-scale coupling through molecular dynamics (MD) simulations of polymeric materials with the aim of improving composite performance for aerospace applications;
- Computational solid mechanics: Nonlocal elastic modeling. Efficient and objective finite-element implementations for Geometrically exact (nonlinear) beam and shell theory. Eigen analysis of transfer matrix for representative unit cell to characterize mechanics of repetitive structures.
- Integration algorithms: Development of integration algorithms for strongly nonlinear oscillators by incorporating analytical techniques in numerical algorithms. Development of accurate integration algorithms for rotational dynamics with short-term quasi-stability properties.
- Probabilistic methods: Probabilistic seismic risk assessment. Probabilistic modeling of microstructure evolution for polycrystals using orientation correlation function.
Broadly: Mechanics, Numerical Methods, and Machine Learning.
MEEM 2150 Mechanics of Materials
MEEM 4405 Introduction to Finite Element Method
MEEM 4150 Intermediate Mechanics of Materials
MEEM 4180 Engineering Biomechanics
MEEM 5990 Applied Machine Learning
MEEM 5170 Finite Element and Variational Methods in Engineering
MEEM 5701 Intermediate Dynamics
- Upendra Yadav (Summer 2022, Immediate Employment: Apple Inc.)
- T. Ponkrshnan
- Shashank Pathrudkar
- Revanth Mattey
- Jonathan Oleson
- Abhishek Keripale
- Sazzad Hossain
- Prathamesh P. Deshpande (Co-Advise with Gregory Odegard)
- Sagar U. Patil (Co-Advise with Gregory Odegard)