Physics-Informed Echo State Networks for Chaotic Systems Forecasting

We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional loss function during the training of the ESNs, which penalizes non-physical predictions without the need of any additional training data. This approach is demonstrated on a chaotic Lorenz system, where the physics-informed ESNs improve the predictability horizon by about two Lyapunov times as compared to conventional ESNs. The proposed framework shows the potential of using machine learning combined with prior physical knowledge to improve the time-accurate prediction of chaotic dynamical systems

Physics-Informed Echo State Networks for Chaotic Systems Forecasting

We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional loss function during the training of the ESNs, which penalizes non-physical predictions without the need of any additional training data. This approach is demonstrated on a chaotic Lorenz system, where the physics-informed ESNs improve the predictability horizon by about two Lyapunov times as compared to conventional ESNs. The proposed framework shows the potential of using machine learning combined with prior physical knowledge to improve the time-accurate prediction of chaotic dynamical systems.Comment: 7 pages, 3 figure

ENTRAP and its potential interaction with European networks

AbstractENTRAP comprises a pan-European cooperation of leading scientific institutions and regulatory bodies in the field of nuclear-waste characterization and its quality assurance for the safe disposal of radioactive waste. Here, the scope of this cooperation is presented and explained and links or interfaces for a potential collaboration with partners fulfilling tasks of IDG-TP are pursued

Identification of key risk factors related to serious road injuries and their health impacts, deliverable 7.4 of the H2020 project SafetyCube

Because of their high number and slower reduction compared to fatalities, serious road injuries are increasingly being adopted as an additional indicator for road safety, next to fatalities. Reducing the number of serious road injuries is one of the key priorities in the EU road safety programme 2011- 2020. In 2013, the EU Member States agreed on the following definition of serious road traffic injuries: a serious road traffic injury is a road traffic casualty with a Maximum AIS level of 3 or higher (MAIS3+). One recommendation created by the EU SUSTAIN project was to conduct “A more detailed study of the causes of serious road injuries, [which] could reveal more specific keys to reduce the number of serious injuries in the EU”. This recommendation is addressed through the identification of crashrelated causation and contributory factors for selected groups of casualties with relatively many MAIS3+ casualties compared to fatalities and groups with a relatively high burden of injury of MAIS3+ casualties. This deliverable is made up of two parts brought together in order to determine the main contributory factors detailed above. This two-step approach initially identifies groups of casualties that are specifically relevant from a serious injury perspective using national level collision and hospital datasets from 6 countries. Following the determination of groups of interest a detailed analysis of the selected groups using indepth data was conducted. On the basis of in-depth data from 4 European countries the main contributory and causal factors are determined for the selected MAIS3+ casualty groups. Alongside the three proceeding deliverables that have formed the major outputs of WP7, deliverable D7.4 is aimed at addressing serious injury policy at an EU levels. As such this report is broadly aimed at policy makers although the inclusion of results from in-depth data analysis also provides information relevant to stakeholders, particularly those working in vehicle design and manufacture or road user behaviour

The development of a multidisciplinary system to understand causal factors in road crashes

The persistent lack of crash causation data to help inform and monitor road and vehicle safety policy is a major obstacle. Data are needed to assess the performance of road and vehicle safety stakeholders and is needed to support the development of further actions. A recent analysis conducted by the European Transport Safety Council identified that there was no single system in place that could meet all of the needs and that there were major gaps including in-depth crash causation information. This paper describes the process of developing a data collection and analysis system designed to fill these gaps. A project team with members from 7 countries was set up to devise appropriate variable lists to collect crash causation information under the following topic levels: accident, road environment, vehicle, and road user, using two quite different sets of resources: retrospective detailed police reports (n=1300) and prospective, independent, on-scene accident research investigations (n=1000). Data categorisation and human factors analysis methods based on Cognitive Reliability and Error Analysis Method (Hollnagel, 1998) were developed to enable the causal factors to be recorded, linked and understood. A harmonised, prospective “on-scene” method for recording the root causes and critical events of road crashes was developed. Where appropriate, this includes interviewing road users in collaboration with more routine accident investigation techniques. The typical level of detail recorded is a minimum of 150 variables for each accident. The project will enable multidisciplinary information on the circumstances of crashes to be interpreted to provide information on the causal factors. This has major applications in the areas of active safety systems, infrastructure and road safety, as well as for tailoring behavioural interventions. There is no direct model available internationally that uses such a systems based approach

The development of a multidisciplinary system to understand causal factors in road crashes

The persistent lack of crash causation data to help inform and monitor road and vehicle safety policy is a major obstacle. Data are needed to assess the performance of road and vehicle safety stakeholders and is needed to support the development of further actions. A recent analysis conducted by the European Transport Safety Council identified that there was no single system in place that could meet all of the needs and that there were major gaps including in-depth crash causation information. This paper describes the process of developing a data collection and analysis system designed to fill these gaps. A project team with members from 7 countries was set up to devise appropriate variable lists to collect crash causation information under the following topic levels: accident, road environment, vehicle, and road user, using two quite different sets of resources: retrospective detailed police reports (n=1300) and prospective, independent, on-scene accident research investigations (n=1000). Data categorisation and human factors analysis methods based on Cognitive Reliability and Error Analysis Method (Hollnagel, 1998) were developed to enable the causal factors to be recorded, linked and understood. A harmonised, prospective “on-scene” method for recording the root causes and critical events of road crashes was developed. Where appropriate, this includes interviewing road users in collaboration with more routine accident investigation techniques. The typical level of detail recorded is a minimum of 150 variables for each accident. The project will enable multidisciplinary information on the circumstances of crashes to be interpreted to provide information on the causal factors. This has major applications in the areas of active safety systems, infrastructure and road safety, as well as for tailoring behavioural interventions. There is no direct model available internationally that uses such a systems based approach

Sheep Updates 2005 - Part 2

This session covers seven papers from different authors: CONCURRENT SESSIONS - STRATEGIC MANAGEMENT 1.Finishing Pastoral Lambs, Peter Tozer, Patricia Harper, Janette Drew, Department of Agriculture Western Australia 2. Coating Improves Wool Quality under Mixed Farming Conditions, KE Kemper, ML Hebart, FD Brien, KS Grimson, DH Smith AMM Ramsay, South Australian Research and Development Institute 3. J. S. Richards, K.D. Atkins, T. Thompson, W. K. Murray, Australian Sheep Industry Co-operative Research Centre and NSW Department of Primary Industries, Orange Agricultural Institute, Forest Rd. Orange 4. Strategic Risk Management in the Sheep Industry, J.R.L. Hall, ICON Agriculture (JRL Hall & Co) 5. Joining Prime Lambs for the Northern End of the Market - a Systems Approach, Chris Carter, Peter Tozer, Department of Agriculture Western Australia 6. Lifetime Wool - Dry feed budgeting tool, Mike Hyder, department of Agriculture Western Australia, John Young, Farming Systems Analysis Service, Kojonup, Western Australia 7. Influence of ultrafine wool fibre curvature and blending with cashmere on attributes of knitwear, B. A. McGregor, Primary Industries Research Victoria, Department of Primary Industries, Victori

Sheep Updates 2005 - Part 4

This session covers twelve papers from different authors: REPRODUCTION 1. Is it worth increasing investment to increase lambing percentages? Lucy Anderton Department of Agriculture Western Australia. 2. What value is a lamb? John Young, Farming Systems Analysis Service, Kojonup, WA 3. Providing twin-bearing ewes with extra energy at lambing produces heavier lambs at marking. Rob Davidson WAMMCO International,, formerly University of Western Australia; Keith Croker, Ken Hart, Department of Agriculture Western Australia, Tim Wiese, Chuckem , Highbury, Western Australia. GENETICS 4. Underlying biological cause of trade-off between meat and wool. Part 1. Wool and muscle glycogen, BM Thomson, I Williams, University of WA, Crawley, JRBriegel, CSIRO Livestock Industries, Floreat Park WA &CRC for the Australian Sheep Industry, JC Greeff, Department of Agriculture Western Australia &CRC for the Australian Sheep Industry. 5. Underlying biological cause of trade-off between meat and wool. Part 2. Wool and fatness, NR Adams1,3, EN Bermingham1,3, JR Briegel1,3, JC Greeff2,3 1CSIRO Livestock Industries, Floreat Park WA 2Department of Agriculture Western Australia, 3CRC for the Australian Sheep Industry 6. Genetic trade-offs between lamb and wool production in Merino breeding programs, Johan Greeff, Department of Agriculture, Western Australia. 7. Clean fleece weight is no phenotypically independent of other traits. Sue Hatcherac and Gordon Refshaugebc aNSWDPI Orange Agricultural Institute, Orange NSW 2800 bUNE c/- NSWDPI Cowra AR&AS Cowra NSW 2794 cAustralian Sheep Industry CRC. 8. When you\u27re on a good thing, do it better: An economic analysis of sheep breed profitability. Emma Kopke, Ross Kingwell, Department of Agriculture, Western Australia, John Young, Farming Systems Analysis Service, Kojonup, WA. 9. Selection Demonstration Flocks: Demonstrating improvementsin productivity of merinos, K.E. Kemper, M.L. Hebart, F.D. Brien, K.S. Jaensch, R.J. Grimson, D.H. Smith South Australian Research and Development Institute 10. You are compromising yield by using Dust Penetration and GFW in breeding programs, Melanie Dowling, Department of Agriculture, Western Australia, A. (Tony) Schlink, CSIRO Livestock Industries, Wembley, Johan Greeff, Department of Agriculture Western Australia. 11. Merino Sheep can be bred for resistance to breech strike. Johan Greeff , John Karlsson, Department of Agriculture Western Australia 12. Parasite resistant sheep and hypersensitivity diarrhoea, L.J.E. Karlsson & J.C. Greeff, Department of Agriculture Western Australi