Session A6: Great Advances in Computational Physics: Past, Present and Future

The path integral picture of quantum systems

The imaginary time path integral ``formalism'' was introduced in 1953 by Feynman to understand the superfluid transition in liquid helium. The equilibrium properties of quantum many body systems is isomorphic to the classical statistical mechanics of cross-linking polymer-like objects. With the Markov Chain Monte Carlo method, invented by Metropolis et al., also in 1953, a potential way of calculating properties of correlated quantum systems was in place. But calculations for many-body quantum systems did not become routine until computers and algorithms had become sufficiently powerful three decades later. Once such simulations could happen, it was realized that simulations provided a deeper insight into boson superfluids, in particular the relation of bose condensation to the polymer end-to-end distance, and the superfluid density to the polymer ``winding number.'' Some recent developments and applications to supersolids, and helium droplets will be given. Finally, limitations of the methodology e.g. to fermion systems are discussed.   

Advances in Monte Carlo computer simulation

Since the invention of the Metropolis method in 1953, Monte Carlo methods have been shown to provide an efficient, practical approach to the calculation of physical properties in a wide variety of systems. In this talk, I will discuss some of the advances in the MC simulation of thermodynamics systems, with an emphasis on optimization to obtain a maximum of useful information.   

Computational Physics and Drug Discovery for Infectious Diseases

This lecture will provide a general introduction to some of the ways that modern computational physics is contributing to the discovery of new pharmaceuticals, with special emphasis on drugs for infectious diseases. The basic sciences and computing technologies involved have advanced to the point that physics-based simulations of drug targets are now yielding truly valuable suggestions for new compounds.   

The need and potential for building a integrated knowledge-base of the Earth-Human system

The pursuit of scientific understanding is increasingly based on interdisciplinary research. To understand more deeply the planet and its interactions requires a progressively more holistic approach, exploring knowledge coming from all scientific and engineering disciplines including but not limited to, biology, chemistry, computer sciences, geosciences, material sciences, mathematics, physics, cyberinfrastucture, and social sciences. Nowhere is such an approach more critical than in the study of global climate change in which one of the major challenges is the development of next-generation Earth System Models that include coupled and interactive representations of ecosystems, agricultural working lands and forests, urban environments, biogeochemistry, atmospheric chemistry, ocean and atmospheric currents, the water cycle, land ice, and human activities.   

Simulating the First Cosmic Structures

Understanding how the first stars and galaxies formed is one of the forefront challenges of modern astrophysics and cosmology. During the last three decades numerical simulations have proven to be a powerful tool in the development and testing of galaxy formation theories. The raw ingredients are the atomic and dark matter that comprise galaxies combined with a well-tested cosmological framework of small-amplitude seed perturbations generated in the early universe. This talk will briefly review progress in galaxy formation simulations and will highlight outstanding issues and prospects for the future.