104,321 research outputs found
Surrogate Modeling of Nonlinear Components and Circuits
Surrogate models are simplified approximations of functions that are described by complex equations. Surrogate modeling of physical systems have been used in various fields such as biology, fluid dynamics, climate modeling, and various other engineering disciplines. This data-driven approach is used to decrease computational cost, decrease computation time, or when the output of a system is diffcult or impossible to measure, by using a \black-box method to approximate the output given inputs. In regards to circuit analysis, surrogate models can be used to decrease computation time and computational load.
In this thesis, surrogate modeling is used to model various nonlinear components and circuits in fREEDA, a multi-physics circuit simulator, for the purpose of speeding up transient analysis. Neural networks are used in place of physics-based equations, resulting in a speedup of 5 - 18x for the evaluation of the components and 3x for the evaluation of entire circuits. The components and circuits tested in this work include: BJT (Bipolar Junction Transistor), MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor), common-emitter amplifier, and common-source amplifier
Quantum Computing for Molecular Biology
Molecular biology and biochemistry interpret microscopic processes in the
living world in terms of molecular structures and their interactions, which are
quantum mechanical by their very nature. Whereas the theoretical foundations of
these interactions are very well established, the computational solution of the
relevant quantum mechanical equations is very hard. However, much of molecular
function in biology can be understood in terms of classical mechanics, where
the interactions of electrons and nuclei have been mapped onto effective
classical surrogate potentials that model the interaction of atoms or even
larger entities. The simple mathematical structure of these potentials offers
huge computational advantages; however, this comes at the cost that all quantum
correlations and the rigorous many-particle nature of the interactions are
omitted. In this work, we discuss how quantum computation may advance the
practical usefulness of the quantum foundations of molecular biology by
offering computational advantages for simulations of biomolecules. We not only
discuss typical quantum mechanical problems of the electronic structure of
biomolecules in this context, but also consider the dominating classical
problems (such as protein folding and drug design) as well as data-driven
approaches of bioinformatics and the degree to which they might become amenable
to quantum simulation and quantum computation.Comment: 76 pages, 7 figure
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
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Evolutionary biology for the 21st century
New theoretical and conceptual frameworks are required for evolutionary biology to capitalize on the wealth of data now becoming available from the study of genomes, phenotypes, and organisms - including humans - in their natural environments.Molecular and Cellular BiologyOrganismic and Evolutionary Biolog
The role of concurrency in an evolutionary view of programming abstractions
In this paper we examine how concurrency has been embodied in mainstream
programming languages. In particular, we rely on the evolutionary talking
borrowed from biology to discuss major historical landmarks and crucial
concepts that shaped the development of programming languages. We examine the
general development process, occasionally deepening into some language, trying
to uncover evolutionary lineages related to specific programming traits. We
mainly focus on concurrency, discussing the different abstraction levels
involved in present-day concurrent programming and emphasizing the fact that
they correspond to different levels of explanation. We then comment on the role
of theoretical research on the quest for suitable programming abstractions,
recalling the importance of changing the working framework and the way of
looking every so often. This paper is not meant to be a survey of modern
mainstream programming languages: it would be very incomplete in that sense. It
aims instead at pointing out a number of remarks and connect them under an
evolutionary perspective, in order to grasp a unifying, but not simplistic,
view of the programming languages development process
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