Krithika Manohar

About

Our group develops mathematical and computational algorithms for data-driven modeling, sparse sensing, and system identification in high-dimensional dynamical systems. Drawing on physics-informed structure, statistical mechanics and spectral methods, we provide performance guarantees, interpretability, and uncertainty quantification for learned models.

Research topics

• Computer science
• Artificial intelligence
• Mathematics
• Algorithm
• Mathematical optimization

Selected publications

• Sparse Principal Component Analysis via Variable Projection

  SIAM Journal on Applied Mathematics · 2020-01-01 · 157 citations

  articleOpen access

  Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating between distinct time scales. We demonstrate a robust and scalable SPCA algorithm by formulating it as a value-function optimization problem. This viewpoint leads to a flexible and computationally efficient algorithm. The approach can further leverage randomized…

  PublisherOA PDFDOI

• Constrained Optimization of Sensor Placement for Nuclear Digital Twins

  IEEE Sensors Journal · 2024-02-28 · 33 citations

  articleOpen accessSenior author

  The deployment of extensive sensor arrays in nuclear reactors is infeasible due to challenging operating conditions and inherent spatial limitations. Strategically placing sensors within defined spatial constraints is essential for the reconstruction of reactor flow fields and the creation of nuclear digital twins. We develop a data-driven technique that incorporates constraints into an optimization framework for sensor placement, with the primary objective of minimizing reconstruction errors un…

  PublisherOA PDFDOI

• Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning

  AIAA Journal · 2021-07-20 · 28 citations

  preprintOpen access

  Data science, and machine learning in particular, is rapidly transforming the scientific and industrial landscapes. The aerospace industry is poised to capitalize on big data and machine learning, which excels at solving the types of multi-objective, constrained optimization problems that arise in aircraft design and manufacturing. Indeed, emerging methods in machine learning may be thought of as data-driven optimization techniques that are ideal for high-dimensional, nonconvex, and constrained,…

  PublisherOA PDFDOI

• PySensors: A Python package for sparse sensor placement

  The Journal of Open Source Software · 2021-02-21 · 23 citations

  articleOpen access

  Successful predictive modeling and control of engineering and natural processes is often entirely determined by in situ measurements and feedback from sensors (S. L. Brunton

  PublisherOA PDFDOI

• Ultrasensitive Capacitive Sensor Composed of Nanostructured Electrodes for Human–Machine Interface

  Advanced Materials Technologies · 2022-03-25 · 16 citations

  article

  Abstract Human–machine interface requires various sensors for communication, manufacturing and environmental control, and health and safety monitoring. Capacitive sensors have been used to detect touch, distance, geometry, electric property, and environmental parameters. However, highly sensitive proximity detection with a small form factor has always been a challenge. This paper presents a capacitive sensor composed of a nanostructured electrode array for contact and noncontact detection. In th…

  PublisherDOI

Recent grants

• PostDoctoral Research Fellowship

  NSF · $150k · 2018–2022

Frequent coauthors

• Steven L. Brunton

  Dynamic Systems (United States)

  33 shared

• J. Nathan Kutz

  22 shared

• Dimitrios Giannakis

  Dartmouth College

  11 shared

• Andrew M. Stuart

  California Institute of Technology

  11 shared

• Dmitry Burov

  California Institute of Technology

  11 shared

• Bingni W. Brunton

  University of Washington

  7 shared

• J. Nathan Kutz

  5 shared

• N. Benjamin Erichson

  5 shared

Education

• Ph.D., Applied Mathematics

  University of Washington

  2018

• B.S., Mathematics and Computer Science

  University of Massachusetts Lowell

  2013

Similar researchers at University of Washington

• Christopher S. Brethertonh-101
• Peter J. Schmidh-55
• Dale Durranh-53