RISE Lab

Foundational Mechanics, Sensing, and Complex Dynamical Systems

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This thrust focuses on foundational mechanics, computational representations, sensing concepts, and complex dynamical systems that support structural health monitoring, digital twins, and resilient infrastructure systems.

The aim is to develop mathematical, mechanical, and computational tools that help represent structural behaviour in ways that are physically meaningful, interpretable, and useful for monitoring, prognosis, and decision support. This thrust also provides space for exploratory work inspired by bio-inspired sensing and active-matter-like collective dynamics where such ideas can inform sensing, modelling, or infrastructure monitoring.

Activities

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This page highlights selected academic activities, invited lectures, workshops, outreach efforts, and professional engagements connected to teaching, research, and the development of RISE Lab.

Workshops and Short Courses
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Upcoming Workshop

Machine Learning for Scientific Computing

Role: Coordinator
Venue: Indian Institute of Technology Bombay
Date: July 23–24, 2026
Status: Upcoming

This workshop introduces machine learning methods for scientific computing and engineering applications, with emphasis on traditional numerical methods, physics-informed learning, surrogate modelling, and data-driven solution of differential equations.

Join

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RISE Lab is actively looking for motivated PhD students, M.Tech students, and highly motivated B.Tech students interested in smart and resilient infrastructure systems.

The group works at the interface of structural engineering, sensing, computer vision, AI/ML, scientific computing, digital twins, structural health monitoring, prognosis, and infrastructure resilience.

Specific project topics are discussed individually after understanding the student’s background, interests, expected time commitment, and available research opportunities.

PhD Students
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I am especially interested in recruiting PhD students who want to work on long-term research problems related to smart infrastructure, structural health monitoring, physics-guided sensing, computer vision, AI/ML, digital twins, scientific machine learning, uncertainty-aware prognosis, and resilient infrastructure systems.

Uncertainty-Aware SHM and Bayesian Inference

Mon, 01 Jan 0001 00:00:00 +0000

This thrust focuses on uncertainty-aware methods for structural health monitoring, damage diagnosis, model updating, prognosis, and decision support.

The goal is to move from deterministic monitoring outputs toward confidence-aware interpretation, where engineers can understand not only what a model predicts, but also how reliable that prediction may be.

What We Study
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Uncertainty-Aware Monitoring

Methods for understanding how measurement noise, modelling assumptions, environmental variability, and limited data affect infrastructure monitoring outcomes.

Team

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RISE Lab — Resilient Infrastructure and Smart Engineering Lab is a growing research group in the Department of Civil Engineering at IIT Bombay.

The group works on smart and resilient infrastructure systems through structural health monitoring, physics-guided sensing, computer vision, AI/ML, digital twins, prognosis, and decision support.

Faculty Lead
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Prof. Ashish Pal
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Assistant Professor

Department of Civil Engineering
Indian Institute of Technology Bombay

Scientific Machine Learning and Equation Discovery

Mon, 01 Jan 0001 00:00:00 +0000

This thrust focuses on scientific machine learning methods for understanding structural and dynamical systems from data.

The emphasis is on combining data-driven learning with mechanics, governing principles, physical constraints, and computational models so that machine learning methods remain meaningful for engineering analysis and infrastructure monitoring.

What We Study
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Scientific Machine Learning for Engineering Systems

Methods that combine machine learning with physical principles, numerical modelling, and engineering knowledge for structural and infrastructure applications.

Teaching

Mon, 01 Jan 0001 00:00:00 +0000

My teaching focuses on building strong foundations in structural engineering, mechanics, numerical methods, and computational thinking. I aim to connect classical civil engineering concepts with modern computational tools, simulations, and research-oriented problem solving.

Courses Taught
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Introduction to Structural Design
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Undergraduate course covering the fundamentals of reinforced concrete behaviour and design, including flexure, shear, serviceability, limit state design philosophy, and practical design considerations for structural members.

Projects

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Research projects at RISE Lab focus on smart and resilient infrastructure systems through sensing, structural health monitoring, computer vision, AI/ML, digital twins, prognosis, and decision support.

The projects listed here represent ongoing sponsored, internal, exploratory, and collaborative research activities.

Research Publications

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The publications below reflect research in structural health monitoring, nonlinear system identification, scientific machine learning, sensor fusion, uncertainty quantification, digital twins, and smart and resilient infrastructure systems.

Prospective PhD and M.Tech students are encouraged to explore these papers to understand the group’s research style, technical foundations, and broader areas of interest.

View research opportunities →

Journal Articles

Latching control: Experimental study on pendulum latched mass damper

Authors: Wang, Hao and Pal, Ashish and Nagarajaiah, Satish and Ke, Shitang and Zhu, Songye

Journal: Engineering Structures (2026)

View Publication →

Signal-based online acceleration and strain data fusion using B-splines and Kalman filter for full-field dynamic displacement estimation

Authors: Das, Aniruddha and Pal, Ashish and Nagarajaiah, Satish and Sajeer, Mohamed M. and Mukhopadhyay, Suparno

Journal: Mechanical Systems and Signal Processing (2026)

View Publication →

Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems

Authors: Pal, Ashish and Bhowmick, Sutanu and Nagarajaiah, Satish

Journal: Mechanical Systems and Signal Processing (2025)

View Publication →

Data fusion based on short-term memory Kalman filtering using intermittent-displacement and acceleration signal with a time-varying bias

Authors: Pal, Ashish and Nagarajaiah, Satish

Journal: Mechanical Systems and Signal Processing (2024)

View Publication →

Sparsity promoting algorithm for identification of nonlinear dynamic system based on Unscented Kalman Filter using novel selective thresholding and penalty-based model selection

Authors: Pal, Ashish and Nagarajaiah, Satish

Journal: Mechanical Systems and Signal Processing (2024)

View Publication →

Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains

Authors: Pal, Ashish and Meng, Wei and Nagarajaiah, Satish

Journal: Sensors (2023)

View Publication →

Subsurface damage detection via noncontact laser based surface level strain sensing smart skin with carbon nanotubes

Authors: Pal, Ashish and Meng, Wei and Bachilo, Sergei M. and Weisman, R. Bruce and Nagarajaiah, Satish

Journal: Engineering Structures (2023)

View Publication →

Hybrid method for full-field response estimation using sparse measurement data based on inverse analysis and static condensation

Authors: Pal, Ashish and Meng, Wei and Nagarajaiah, Satish

Journal: Journal of Infrastructure Intelligence and Resilience (2022)

View Publication →

Next-generation 2D optical strain mapping with strain-sensing smart skin compared to digital image correlation

Authors: Meng, Wei and Pal, Ashish and Bachilo, Sergei M. and Weisman, R. Bruce and Nagarajaiah, Satish

Journal: Scientific Reports (2022)

View Publication →

Peak Factor--Based Modal Combination Rule of Response-Spectrum Method for Peak Floor Accelerations

Authors: Pal, Ashish and Gupta, Vinay K

Journal: Journal of Structural Engineering (2021)

View Publication →

A note on spectral velocity approximation at shorter intermediate periods

Authors: Pal, Ashish and Gupta, Vinay K

Journal: Soil Dynamics and Earthquake Engineering (2021)

View Publication →

Conference Papers

Modal Identification and Damage Detection of Railway Bridges Using Time-Varying Modes Identified from Train Induced Vibrations

Authors: Pal, Ashish and Gaur, Astha and Mukhopadhyay, Suparno

Conference: Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020 (2020)

Books / Book Chapters

Deep Learning for Image Segmentation and Subsurface Damage Detection Based on Full-Field Surface Strains

Authors: Pal, Ashish and Meng, Wei and Nagarajaiah, Satish

Book: Proceedings of the Society for Experimental Mechanics Series (2023)

Next-Generation Non-contact Strain-Sensing Method Using Strain-Sensing Smart Skin (S4) for Static and Dynamic Measurement

Authors: Meng, Wei and Pal, Ashish and Bachilo, Sergei M. and Weisman, R. Bruce and Nagarajaiah, Satish

Book: Model Validation and Uncertainty Quantification, Volume 3 (2023)

View Publication →

arXiv Preprints

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems

Authors: Pal, Ashish and Nagarajaiah, Satish

arXiv: (2024)

View Publication →

System Identification and Digital Twin Updating

Mon, 01 Jan 0001 00:00:00 +0000

This thrust focuses on identifying structural behaviour, estimating hidden response quantities, and updating computational models using monitoring data.

The long-term objective is to support digital twins that are not only visual representations, but data-informed and mechanics-aware models that can evolve with measurements from real civil infrastructure systems.

What We Study
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Structural State Estimation

Methods for estimating important structural response quantities from measured data, especially when measurements are noisy, sparse, indirect, or incomplete.

Damage Detection, Localization, and Condition Assessment

Mon, 01 Jan 0001 00:00:00 +0000

This thrust focuses on methods for detecting, localizing, and interpreting damage in civil infrastructure systems. The goal is to develop damage diagnosis approaches that are connected to structural response, deformation patterns, stiffness changes, vibration behaviour, and engineering interpretation.

Rather than relying only on black-box classification, the work emphasizes physically meaningful indicators, interpretable models, and decision-support methods for infrastructure condition assessment.

What We Study
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Damage-Sensitive Structural Response

Methods for using measured or simulated structural response to identify abnormal behaviour, stiffness changes, local deterioration, and possible damage-sensitive patterns.

Research

Mon, 01 Jan 0001 00:00:00 +0000

Research Vision
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My research develops physics-guided intelligent systems for sensing, interpreting, modelling, and decision-making in civil infrastructure.

The central idea is to combine structural dynamics, sensing technologies, scientific computing, and artificial intelligence so that structural health monitoring moves beyond data collection toward diagnosis, prediction, uncertainty-aware interpretation, and practical decision support.

Sensing Mechanics Structural Dynamics AI/ML Digital Twins Decision Support

Research Thrust Areas
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About

Mon, 01 Jan 0001 00:00:00 +0000

About Prof. Ashish Pal
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Prof. Ashish Pal is an Assistant Professor in the Department of Civil Engineering at the Indian Institute of Technology Bombay. He leads RISE Lab — Resilient Infrastructure and Smart Engineering Lab, a research group developing sensing, AI, and digital-twin methods for smart and resilient civil infrastructure systems.

His work brings together structural engineering, sensing technologies, computer vision, machine learning, scientific computing, and digital twins. A central theme of his research is to develop intelligent methods that are not only data-driven, but also guided by physical principles and engineering knowledge.

Physics-Guided Sensing and Signal Processing for SHM

Mon, 01 Jan 0001 00:00:00 +0000

This thrust focuses on sensing, signal processing, and computer vision methods for structural health monitoring of civil infrastructure. The goal is to extract reliable structural information from measurements that may be noisy, incomplete, indirect, or collected under field constraints.

A central idea is to combine measured data with structural dynamics, mechanics, and engineering knowledge so that sensing outputs remain physically meaningful and useful for infrastructure monitoring.

What We Study
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Structural Response Measurement

Methods for estimating displacement, vibration, strain, modal response, and other structural quantities from sensor and vision-based measurements.

Contact

Mon, 01 Jan 0001 00:00:00 +0000

I welcome research discussions, student enquiries, academic collaborations, invited talks, workshops, and industry-facing conversations related to smart and resilient infrastructure systems, structural health monitoring, physics-guided sensing, computer vision, AI/ML, digital twins, prognosis, and decision support.

Academic Affiliation
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Prof. Ashish Pal
Assistant Professor
Department of Civil Engineering
Indian Institute of Technology Bombay
Powai, Mumbai, Maharashtra, India, 400076

Email
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For academic, research, teaching, collaboration, and student-related communication, please contact me through my institutional email.

RISE Lab

Foundational Mechanics, Sensing, and Complex Dynamical Systems

Activities

Workshops and Short Courses #

Machine Learning for Scientific Computing

Join

PhD Students #

Uncertainty-Aware SHM and Bayesian Inference

What We Study #

Uncertainty-Aware Monitoring

Team

Faculty Lead #

Prof. Ashish Pal #

Scientific Machine Learning and Equation Discovery

What We Study #

Scientific Machine Learning for Engineering Systems

Teaching

Courses Taught #

Introduction to Structural Design #

Projects

Sponsored Research Projects #

High-Fidelity Digital Twins via Scientific Discovery Using Physics-Informed Machine Learning and Uncertainty Quantification for Structural Health Monitoring and Performance Assessment

Research Publications

Journal Articles

Latching control: Experimental study on pendulum latched mass damper

Signal-based online acceleration and strain data fusion using B-splines and Kalman filter for full-field dynamic displacement estimation

Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems

Data fusion based on short-term memory Kalman filtering using intermittent-displacement and acceleration signal with a time-varying bias

Sparsity promoting algorithm for identification of nonlinear dynamic system based on Unscented Kalman Filter using novel selective thresholding and penalty-based model selection

Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains

Subsurface damage detection via noncontact laser based surface level strain sensing smart skin with carbon nanotubes

Hybrid method for full-field response estimation using sparse measurement data based on inverse analysis and static condensation

Next-generation 2D optical strain mapping with strain-sensing smart skin compared to digital image correlation

Peak Factor--Based Modal Combination Rule of Response-Spectrum Method for Peak Floor Accelerations

A note on spectral velocity approximation at shorter intermediate periods

Conference Papers

Modal Identification and Damage Detection of Railway Bridges Using Time-Varying Modes Identified from Train Induced Vibrations

Books / Book Chapters

Deep Learning for Image Segmentation and Subsurface Damage Detection Based on Full-Field Surface Strains

Next-Generation Non-contact Strain-Sensing Method Using Strain-Sensing Smart Skin (S4) for Static and Dynamic Measurement

arXiv Preprints

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems

System Identification and Digital Twin Updating

What We Study #

Structural State Estimation

Damage Detection, Localization, and Condition Assessment

What We Study #

Damage-Sensitive Structural Response

Research

Research Vision #

Research Thrust Areas #

About

About Prof. Ashish Pal #

Physics-Guided Sensing and Signal Processing for SHM

What We Study #

Structural Response Measurement

Contact

Academic Affiliation #

Email #

Workshops and Short Courses
#

PhD Students
#

What We Study
#

Faculty Lead
#

Prof. Ashish Pal
#

What We Study
#

Courses Taught
#

Introduction to Structural Design
#

Sponsored Research Projects
#

What We Study
#

What We Study
#

Research Vision
#

Research Thrust Areas
#

About Prof. Ashish Pal
#

What We Study
#

Academic Affiliation
#

Email
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