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Shilpi Singh
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DR. SHILPI SINGH

PhD in Engineering Physics from Aalto University, my expertise includes quantum
computing, statistics, single-electron devices, and machine learning.
My Blog


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MY RECENT BLOG POSTS

I enjoy painting and exploring various art forms, which allows me to express
creativity and find balance outside my research work.


RANDOM FIRST POST

July 28, 2024

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describing your block. Any text will do.…


QUOD ET UT DEBITIS.

July 19, 2024

Vero dolor praesentium dignissimos id blanditiis tempora amet. Sint a voluptate
vel sapiente. Quis dolore nam voluptatem asperiores autem aut…


ULLAM RERUM PERSPICIATIS NATUS LABORE.

July 19, 2024

Ut voluptas quisquam itaque eos. Soluta hic rerum labore quis quis non incidunt
omnis. Autem voluptatem quisquam hic rerum. Ipsa…

All Blog Posts


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MY PUBLICATIONS

My research publications focus on quantum mechanics, single-electron devices,
and rare event statistics. Notable works include studies on electronic double
dots and current fluctuations in bistable conductors.


MARTINGALES FOR PHYSICISTS: A TREATISE ON STOCHASTIC THERMODYNAMICS AND BEYOND


É. ROLDÁN, I. NERI, R. CHETRITE, S. GUPTA, S. PIGOLOTTI, F. JULICHER, K.
SEKIMOTO

Adv. Phys. 1-258 (2024)

We review the theory of martingales as applied to stochastic thermodynamics and
stochastic processes in physics more generally. A 258 tutorial review on
Martingale theory and its applications to physics (with emphasis on stochastic
thermodynamics), population dynamics, finance, and beyond.


WHAT TO LEARN FROM FEW VISIBLE TRANSITIONS’ STATISTICS?


P. E. HARUNARI, A. DUTTA, M. POLETTINI,  AND É. ROLDÁN

Adv. Phys. 1-258 (2024)

We consider an observer who records a time series of occurrences of one or
several transitions performed by a system, under the single assumption that its
underlying dynamics is Markovian. We pose the question of how one can use the
transitions’ information to make inferences of dynamical, thermodynamical, and
biochemical properties. First, putting forward first-passage time techniques, we
derive analytical expressions for the probabilities of consecutive transitions
and for the time elapsed between them, which we call inter-transition times.
Second, we develop an estimate lower bound to the entropy production rate which
can be split into two non-negative contributions, one due to the statistics of
transitions and a second due to the statistics of inter-transition times. We
also show that when only one current is measured, our estimate still detects
irreversibility even in the absence of net currents in the transition time
series. While entropy production is entailed in the statistics of two successive
transitions of the same type (i.e. repeated transitions), the statistics of two
different successive transitions (i.e. alternated transitions) can probe the
existence of an underlying disorder in the motion of e.g. molecular motors.


NONRECIPROCAL FORCES ENABLE COLD-TO-HOT HEAT TRANSFER BETWEEN NANOPARTICLES


S. A. M. LOOS, S. ARABHA, A. RAJABPOUR, A. HASSANALI, É. ROLDÁN

Sci. Rep. 13, 4517 (2023)

We study the heat transfer between two nanoparticles held at different
temperatures that interact through nonreciprocal forces, by combining molecular
dynamics simulations with stochastic thermodynamics. Our simulations reveal that
it is possible to construct nano refrigerators that generate a net heat transfer
from a cold to a hot reservoir at the expense of power exerted by the
nonreciprocal forces. Applying concepts from stochastic thermodynamics to a
minimal underdamped Langevin model, we derive exact analytical expressions
predictions for the fluctuations of work, heat, and efficiency, which reproduce
thermodynamic quantities extracted from the molecular dynamics simulations. The
theory only involves a single unknown parameter, namely an effective friction
coefficient, which we estimate fitting the results of the molecular dynamics
simulation to our theoretical predictions. Using this framework, we also
establish design principles which identify the minimal amount of entropy
production that is needed to achieve a certain amount of uncertainty in the
power fluctuations of our nano refrigerator. Taken together, our results shed
light on how the direction and fluctuations of heat flows in natural and
artificial nano machines can be accurately quantified and controlled by using
nonreciprocal forces.

My Publications



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