Physics Aspects of the UK Nuclear Innovation Programme
Digital Reactor Design
Dr Audrey Alejandro, Assistant Professor, Department of Methodology, London School of Economics.
Audrey, an expert on discourse and language, will introduce participants to the role of discourse and communication regarding issues such as climate change. How does language legitimise certain forms of knowledge to the detriment of others? How are certain concerns framed in a way that put them on the political agenda and generate social action? Reflecting on these questions (and others!) will help the focus groups to take into account the discursive, linguistic and communicational dimensions of climate change in their discussion.
Dr John T Bruun, Research Fellow, Climate Dynamics, University of Exeter & Chair of IOP Physics Communicators Group and meeting host.
John, a theoretical physicist, focusses on developing an even better understanding of the climate system. He will introduce the latest thinking on the physics of the climate, help to articulate what the current controversies are, and suggest potential futures we can actively participate in from a physics and society perspective.
Professor Spiros Evangelou, Physics Department, University of Ioannina, Ioannina, Greece.
Spiros is theoretical physicist who specialises in quantum mechanics, wave transport theory including Anderson localization and system universality. He will share thoughts about the role of breakthrough thinking in physics that promote innovative ways to understand random and chaotic processes in nature, including the potentials to further enhance our understanding of the physics of the climate system.
Guillaume Wright, Senior Publisher, IOP Publishing, Environmental Research Letters.
The way we promote, develop public understanding and publish our science around the contemporary issues of climatic change is at the heart of how we explain the physics of climate. This is encapsulated in the IOP’s strategy. Guillaume will share this perspective and the ethics of how we approach this in how we publish and disseminate our work across all our communities.
Professor Peter Young, Emeritus Professor, Lancaster Environment Centre, Lancaster University.
Dynamic systems analysis and data-based mechanistic modelling (DBM): what opportunities do they provide for climate science. Peter is an expert in the data-based identification of linear, non-linear, and chaotic dynamical system processes in the environmental sciences. His work spans signal and system analysis methods that enable us to discover how systems work, based on the available data, and so can enhance our ability to predict and forecast physical consequences. He will share a perspective on how we approach system analysis with a focus on the opportunities it provides to climate science.
Environmental Physics Day: Oceans
The Ocean: brake, accelerator and warning lights of the climate system
The role of the ocean in climate variability
What else does the ocean do for us? From carbon storage to food production
Investigating the role of the ocean in past climate events: The Day After Tomorrow, Yester-day
Professor Gerry Lander, Institut Laue Langevin, Grenoble, France
Training Neural Networks for Physics applications, an overview and application to RBS automation
A fourth paradigm? The challenges of using machine learning in physics
Data-driven discovery of functional materials
Bayesian inference in condensed matter physics - learning force fields and wave functions
Machine learning in soft & biological matter (afternoon session I)
Interpolation of intermolecular potentials via machine learning
Inferring the hidden rules of cellular behaviour through computer vision and deep learning
Adventures with Gaussian process emulators for uncertainty and sensitivity analysis for cardiac models
Physics, Nous and Computation
Assessment of low doserate intraprostatic brachytherapy with automated segmentation and registration between clinical imaging
Machine learning in plasma & accelerator physics (afternoon session II)
Decisions, decisions:The role of artificial decision making in bridging the gap between data and design in plasma chemical processes
Data Driven Modelling of Plasma in Tokamaks
CLARA-Net: A framework for integrating accelerator experiments, simulations and machine learning
Neural Neworks for RF-breakdown detection