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Kopczewska K: Modelling Spatial Density

Modelling Spatial Density

Data, Methods, and R Applications in Statistics, Econometrics, and Machine Learning

This textbook offers a concise but self-contained introduction to the art of numerical modelling in sciences. It discusses all the steps, from the mathematical foundations of the model to the solution procedures that are commonly used by advanced practitioners.

Introduction to Numerical Modeling in the Earth Sciences

Integral Manifolds for Impulsive Differential Problems with Applications offers readers a comprehensive resource on integral manifolds for different classes of differential equations which will be of prime importance to researchers in applied mathematics, engineering, and physics. The book offers a highly application-oriented approach, reviewing the qualitative properties of integral manifolds which have significant practical applications in emerging areas such as optimal control, biology, mechanics, medicine, biotechnologies, electronics, and economics. For applied scientists, this will be an important introduction to the qualitative theory of impulsive and fractional equations which will be key in their initial steps towards adopting results and methods in their research.

Integral Manifolds for Impulsive Differential Problems with Applications

Emerging Materials and Technologies

This book explains the physical principles and theoretical basis of acoustics of nanofluids with mathematical rigor, focusing on concepts and points of view that have proven effective in applications such as heat transfer, petroleum science and technology, wastewater treatment, food processing, and hydrogen production.

This book explains the physical principles and theoretical basis of acoustics of nanofluids with mathematical rigor, focusing on concepts and points of view that have proven effective in applications such as heat transfer, petroleum science and technology, wastewater treatment, food processing, and hydrogen production.

Thermo-Acoustics of Nanofluids and Transfer Processes

Chapman & Hall/CRC The Python Series

Python for Mathematics introduces readers to effective methods for doing mathematics using the Python programming language

Python for Mathematics

A Comprehensive Practical Guide for Graduates and Young Academics in Science and Engineering

The Doctorate Blueprint

This textbook offers an introduction to deep learning for solving inverse problems. It introduces deep neural networks and deep neural network based signal and image reconstruction techniques. It discusses robustness aspects, how to evaluate and test different methods, and data-centric aspects.

This textbook offers an introduction to deep learning for solving inverse problems. It introduces deep neural networks and deep neural network based signal and image reconstruction techniques. It discusses robustness aspects, how to evaluate and test different methods, and data-centric aspects.

Deep Learning for Computational Imaging

Bring mathematical principles to bear on engineering problems with this updated text The evolution of industrial processes has resulted in greater emphasis upon analytical and numerical problem solving. Process improvement through experimentation is impractical and consequently engineers must rely upon computational and technical analysis. Furthermore, the ease with which time-series data can be collected and processed has made harmonic signal interpretation routine. Thus, the ability of engineers to analyze, model, compute, and interpret process phenomena is crucial to professional practice. Problem Solving in Engineering meets these needs with a foundational introduction to mathematical techniques in applied sciences and engineering. Incorporating examples from a range of scientific fields, it communicates principles that can be adapted to many hardware-software combinations. Now fully updated to reflect the latest research and applications, it remains an essential tool for engineers and applied scientists everywhere. Readers of the second edition will also find: <ul><li>Extensive time devoted to problem formulation</li><li>Detailed discussion of integro-differential equations and the processing and analysis of time-series data</li><li>The use of vorticity transport for the solution of momentum, heat, and mass transfer problems in two dimensions</li><li>Examples and problems drawn from aviation, telegraphy, structural failures, railroad operation, chemical processes, automatic process control, seismology, neutron diffusion, gravitation, and quantum theory</li><li>Many additional narrative-type exercises written to appeal to students who find problems in context better suited to their learning style</li><li>Solutions manual available for qualified instructors</li></ul> Problem Solving in Engineering is ideal for advanced undergraduate, graduate students, and technical professionals in the physical sciences, specifically chemical, civil, biochemical, electrical, and mechanical engineering, as well as physics, chemistry, and biology.

Analytical Mathematics and Numerical Analysis

Problem Solving in Engineering

This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.

Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems

Materials, Devices, and Circuits

This book will provide an overview of emerging semiconductor devices and their applications in electronic circuits, which form the foundation of electronic devices. The area of low-power devices and circuits has gained popularity on a global basis due to the need for the current scenario of the IC industry.

Device Circuit Co-Design Issues in FETs

Probability Theory And Stochastic Processes With Applications

Oxford Series in Ecology and Evolution

Aimed at a broad audience of graduate students and researchers in ecology and evolution, this novel creates a persuasive argument that an explicit consideration of history will often lead to a deeper, more nuanced understanding of almost every eco-evolutionary system.

Aimed at a broad audience of graduate students and researchers in ecology and evolution, this novel creates a persuasive argument that an explicit consideration of history will often lead to a deeper, more nuanced understanding of almost every eco-evolutionary system.

Historical Approaches to Ecology and Evolution

Beyond Equilibria

This book covers a broad range of questions related to the importance of cell geometry in many areas, including nuclear positioning, chromosome dynamics, cytoskeletal remodeling, tissue patterning, cell-matrix and multicellular contacts. It also addresses medical implications, from its implication in the development of tumors to its role in fibrosis. Together, the chapters in this book will highlight current knowledge and modern approaches to investigating the biophysical design principles underlying key biological functions, with the overall goal of elucidating the connections between cell geometry and nuclear mechanics.

This book covers a broad range of questions related to the importance of cell geometry in many areas, including nuclear positioning, chromosome dynamics, cytoskeletal remodeling, tissue patterning, cell-matrix and multicellular contacts. It also addresses medical implications, from its implication in the development of tumors to its role in fibrosis. Together, the chapters in this book will highlight current knowledge and modern approaches to investigating the biophysical design principles underlying key biological functions, with the overall goal of elucidating the connections between cell geometry and nuclear mechanics.

Cell Geometry

Modelling Spatial Density

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Bibliografische Angaben

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