Bøker av Ariel Fernandez

Dealing with artificial intelligence, this book delineates AI's role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multi-scale hierarchies hitherto considered off limits for data science.

The dark universe contains matter and energy unidentifiable with current physical models, accounting for 95% of all matter and energetic equivalent in the Universe. The enormous surplus brings up daunting enigmas, such as the cosmological constant problem, and so coming to grips with the invisible Universe has become a scientific imperative.This book addresses this need, reckoning that no cogent physical model of the dark universe can be implemented without first addressing the metaphysical hurdles along the way. The foremost problem is identifying the topology of the universe which, as argued in the book, is highly relevant to identifying secrets of the dark universe.Artificial Intelligence (AI) is a valuable tool in this effort since it can reconcile conflicting data from deep space with the extant laws of physics by building models to decipher the dark universe. This book explores the applications of AI and how it can be used to embark on a metaphysical quest to identify the topology of the universe as a prerequisite to implementing a physical model of the dark universe.The book is intended for a broad readership, but a background in college-level physics and computer science is essential. The book will be a valuable guide for graduate students as well as researchers in physics, astrophysics and computer science focusing on the applications of artificial intelligence in unravelling the nature of the dark universe.Key Features: - Provides readers with an intellectual toolbox to understand physical arguments on dark matter and energy- Up-to-date with the latest, cutting edge research- Authored by an expert on artificial intelligence and mathematical physicsAriel Fernández (born Ariel Fernández Stigliano, April 8, 1957) is an Argentine-American physical chemist and mathematician. He obtained a Ph. D. degree in Chemical Physics from Yale University in record time and held the Karl F. Hasselmann Endowed Chair Professorship in Engineering at Rice University until his retirement. He was also an Adjunct Professor of Computer Science at the University of Chicago. To date, he has published approximately 500 scientific papers in professional journals and has also authored nine books on physical chemistry, molecular medicine, artificial intelligence, mathematical cosmology and mathematical physics. Additionally, he holds several patents on technological innovation. Fernández is a member of the National Research Council of Argentina (CONICET) and, since 2018, heads the Daruma Institute for Applied Intelligence, the research arm of AF Innovation, a Consultancy based in Argentina and the US.

As we prod the cosmos at large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book resorts to artificial intelligence for answers and describes the outcome of this quest in terms of an ur-universe.

Dealing with artificial intelligence, this book delineates AI's role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multi-scale hierarchies hitherto considered off limits for data science.

In the era of big biomedical data, there are many ways in which artificial intelligence (AI) is likely to broaden the technological base of the pharmaceutical industry. Cheminformatic applications of AI involving the parsing of chemical space are already being implemented to infer compound properties and activity. By contrast, dynamic aspects of the design of drug/target interfaces have received little attention due to the inherent difficulties in dealing with physical phenomena that often do not conform to simplifying views.This book focuses precisely on dynamic drug/target interfaces and argues that the true game change in pharmaceutical discovery will come as AI is enabled to solve core problems in molecular biophysics that are intimately related to rational drug design and drug discovery.Here are a few examples to convey the flavor of our quest: How do we therapeutically impair a dysfunctional protein with unknown structure or regulation but known to be a culprit of disease? In regards to SARS-CoV-2, what is the structural impact of a dominant mutation?, how does the structure change translate into a fitness advantage?, what new therapeutic opportunity arises? How do we extend molecular dynamics simulations to realistic timescales, to capture the rare events associated with drug targeting in vivo? How do we control specificity in drug design to selectively remove side effects? This is the type of problems, directly related to the understanding of drug/target interfaces, that the book squarely addresses by leveraging a comprehensive AI-empowered approach.