- Shape Memory Alloy Transformations
- Advanced Materials and Mechanics
- Advanced Mathematical Modeling in Engineering
- Composite Material Mechanics
- Ferroelectric and Piezoelectric Materials
- Adhesion, Friction, and Surface Interactions
- Acoustic Wave Resonator Technologies
- Advanced Sensor and Energy Harvesting Materials
- Structural Analysis and Optimization
- Microstructure and mechanical properties
- Liquid Crystal Research Advancements
- Microstructure and Mechanical Properties of Steels
- Numerical methods in engineering
- Force Microscopy Techniques and Applications
- Model Reduction and Neural Networks
- Topology Optimization in Engineering
- Cosmology and Gravitation Theories
- Titanium Alloys Microstructure and Properties
- Dielectric materials and actuators
- Multiferroics and related materials
- Metal and Thin Film Mechanics
- Elasticity and Material Modeling
- High-Velocity Impact and Material Behavior
- Magnesium Alloys: Properties and Applications
- Machine Learning in Materials Science
California Institute of Technology
2015-2024
Indian Institute of Technology Kanpur
2021-2024
Institut Jean Le Rond d'Alembert
2021
University of Calcutta
2010-2020
University of Minnesota
1991-2019
Pasadena City College
2007-2018
Center for Nanoscale Science and Technology
2008
Northrop Grumman (United States)
2008
Tata Consultancy Services (India)
2007
Bhabha Atomic Research Centre
2005
1. Introduction 2. Review of Continuum Mechanics 3. Theory Crystalline Solids 4. Martensitic Phase Transformation 5. Twinning in Martensite 6. Origin Microstructure 7. Special Microstructures 8. Analysis 9. The Shape-Memory Effect 10. Thin Films 11. Geometrically Linear 12. Piece-wise Elasticity 13. Polycrystals
The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this been generalized to operators that learn function For partial differential equations (PDEs), directly the mapping from any functional parametric dependence solution. Thus, they an entire family PDEs, in contrast methods which solve one instance equation. In work, we formulate a new operator by parameterizing integral kernel Fourier space,...
The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and set classes, or two spaces. purpose this work is to generalize so that they can learn infinite-dimensional spaces (operators). key innovation in our single network parameters, within carefully designed architecture, may be used describe different approximations those We formulate approximation the mapping by composing nonlinear activation functions class integral...
We develop a general framework for data-driven approximation of input-output maps between infinite-dimensional spaces. The proposed approach is motivated by the recent successes neural networks and deep learning, in combination with ideas from model reduction. This results network which, principle, defined on spaces and, practice, robust to dimension finite-dimensional approximations these required computation. For class maps, suitably chosen probability measures inputs, we prove convergence...
The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or sets. We propose a generalization to learn operators, termed that map infinite function spaces. formulate the operator as composition linear integral operators and nonlinear activation functions. prove universal approximation theorem for our proposed operator, showing it can approximate any given continuous operator. are also discretization-invariant, i.e.,...
Snap-through mechanisms are pervasive in everyday life biological systems, engineered devices, and consumer products. transitions can be realized responsive materials via stimuli-induced mechanical instability. Here, we demonstrate a rapid powerful snap-through response liquid crystalline elastomers (LCEs). While LCEs have been extensively examined as material actuators, their deformation rate is limited by the second-order character of phase transition. In this work, locally pattern...
Efforts are under way to manufacture electromechanical devices at ever smaller sizes. However, standard semiconductor technology places limits on how small such machines can be. In their Perspective, Bhattacharya and James explain martensitic materials--which distort reversibly without any diffusion taking place--may overcome this problem. Theoretical studies preliminary experiments suggest that materials act as themselves, the need for complicated construction of moving parts in device.
We demonstrate control of the surface plasmon polariton wavevector in an active metal-dielectric plasmonic interferometer by utilizing electrooptic barium titanate as dielectric layer. Arrays subwavelength interferometers were fabricated from pairs parallel slits milled silver on thin films. Plasmon-mediated transmission incident light through is modulated external voltage applied across film. Transmitted modulation ascribed to two effects, electrically induced domain switching and index.
Shape-memory behavior is the ability of certain materials to recover, on heating, apparently plastic deformation sustained below a critical temperature. Some have good shape-memory as single crystals but little or none polycrystals, while others display even polycrystals. In this paper, we propose theoretical explanation for difference: show that recoverable strain in polycrystal depends texture polycrystal, transformation underlying martensitic and especially change symmetry during...
Abstract This paper examines the domain patterns and macroscopic behaviour of single crystals ferroelectric material using a theory based on energy minimization. A low-energy path is identified for switching novel configuration that yields very large electrostriction identified.
By combining the high-dielectric copper phthalocyanine oligomer (PolyCuPc) and conductive polyanline (PANI) within polyurethane (PU) matrix an all-organic three-component dielectric-percolative composite with high dielectric constant is demonstrated. In this system, high-dielectric-constant PolyCuPc particulates enhance of PU combined two-component in turn serves as host for PANI to realize percolative phenomenon further response. As a result, electromechanical strain 9.3% elastic energy...
We discuss methods of reversibly inducing non-developable surfaces from flat sheets material at the micro-scale all way to macroscopic objects. analyse elastic ground states a nematic glass in membrane approximation as function temperature for disclination defects topological charge +1. An aim is show that by writing an appropriate director field into such solid, one could create surface with Gaussian curvature, dynamically switchable while avoiding stretch energy. In addition prospect...
The effective adhesive properties of heterogeneous thin films are characterized through a combined experimental and theoretical investigation. By bridging scales, we show how variations elastic or at the microscale can significantly affect peeling behavior macroscale. Our study reveals three elementary mechanisms in systems involving front propagation: (i) patterning bending stiffness film produces fluctuations driving force resulting dramatically enhanced resistance to peeling; (ii)...
One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data desired structure neural networks. Graph networks (GNNs) have gained popularity this area since graphs offer a natural way modeling particle interactions provide clear discretizing continuum models. However, constructed approximating such tasks usually ignore long-range due to unfavorable scaling computational...