A Thought on Intelligent Computing Approaches to Challenging Problems Toshinori Munakata 宗像俊則 URLhttp://cis.csuohio.edu/~munakata/

AI Techniques Challenging Problems Apply AI Techniques Functional type Specific technique Pattern recognition Evolutionary computing (GA) Optimization Neural networks......

Challenging Problems There are many, probably thousands, but we consider: o Analysis of Earthquake Prediction o Data Analysis for Volcanic Eruption Prediction o Analysis of Photosynthesis ( 光合成 ) o Tornado ( 竜巻 ) Prediction o Thermonuclear Fusion ( 熱核融合 ) Research Each of these problems is challenging (which means hard), but if solved, is possibly worth a Nobel prize.

Recent successful AI techniques reported 1.Neil Savage. Automatic Scientific Discovery, CACM, May 2012, p Waltz, D. and Buchanan, B.G. Automating science, Science 324, 5923, April 3, Basically an application of evolutionary computing (GA)

Neil Savage. Automatic Scientific Discovery, CACM, May 2012, p. 9. “The team fed their algorithm with experimental data about yeast metabolism, along with theoretical models in the form of sets of equations that could fit the data. The team seeded the program with approximately 1,000 equations, all of which had the correct mathematical syntax but were otherwise random.” Basically an application of evolutionary computing (GA)

o Analysis of Earthquake Prediction Every earthquake is unique and very nonlinear. This means that trying to derive general and universal techniques applicable to all cases is probably not productive. In Munakata’s opinion, researchers give up too soon. All earthquakes Predictable earthquakes

Animal behavior Electro-magnetic observation (e.g., 服部 克巳 千葉大学理学部 地球科学科 教授 – 電離層異常 )... __________________________________________ Phase I. Collection of past observed physical and chemical measurement data and existence and non-existence of earthquakes. Phase II. Application of AI techniques to analyze the observed data.

o Data Analysis for Volcanic Eruption Prediction e.g., Mt. Fuji Should be easier than earthquake prediction (narrower geographical area, narrower focus on types of measurement)

Types of measurement (Wikipedia) 2.1 Seismic Waves (Seismicity) General principles of volcano seismology Seismic case studies Iceberg tremors 2.2 Gas emissions 2.3 Ground deformation 2.4 Thermal monitoring 2.5 Hydrology 2.6 Remote Sensing 2.7 Mass movements and mass failures Collect past data for the above measurements (inputs) and subsequent volcanic eruptions with their intensities (outputs). Determine mapping from the inputs to the outputs by AI techniques, e.g., ID3, backprop, etc. Once mapping is determined, it can be used for real-time prediction.

o Analysis of Photosynthesis ( 光合成 ) A “breakthrough” was reported in “Plant Life’s Boxy Heart,” Science, 334, 1630, December 23, Our basic idea: Collect current theories and equations to represent photosynthesis process. Represent these equations with parameters (if necessary, rewrite or modify the equations). Generate many (e.g., 1000) different versions of these equations by e.g., slightly modifying parameter values. Apply AI techniques (e.g., evolutionary computing) to determine a best set of parameter values. Repeat the last two steps if necessary. )

o Tornado ( 竜巻 ) Prediction This is a major problem in the U.S. Tornado prediction models do exist and they give decent forecast for tornadoes. We want to improve the current forecast (e.g., earlier warning, more accurate area description) by employing more powerful computers, better algorithms (e.g., AI-based, e.g., parameter optimization), and faster communication. For recent hurricane forecast two weather models were employed: the US and European. The latter performed much better. The much higher computing power used for the European model is credited for the difference. e.g., in the European model, much finer mesh points were considered for calculation. It is conceivable to transmit weather data from the U.S. to Japan, compute prediction by a supercomputer (such as 京 ), and send back the resulting forecast to the U.S.

Thermonuclear fusion （核融合研究） e.g., D + T → He + n + energy

臨界プラズマ条件 : D-T 反応（重水素と三重水素の反 応）では「発電炉内でプラズマ温度 1 億℃以上、密度 100 兆個／ cm3 、 1 秒間以上閉じ込めることが条件」重水素三重水素

LHD （ Large Helical Device 、大型ヘリカル装置）は核融合研究の為 に日本の自然科学研究機構核融合科学研究所 (NIFS) の LHD プロジェク トによって製作された大型のヘリカル・ヘリオトロン型のプラズマ装 置。日本独自のヘリカル型磁場方式が用いられ、 1 時間以上にも亘る長 時間のプラズマ持続や、 個 / cm 3 の高密度プラズマを成功させ た。 核融合科学研究所 大型ヘリカル装置計画 /

AI applications GA to determine optimal parameter set for magnetic field configuration NN to perform fine tuning for thermonuclear reactors e.g., magnetic field configuration intensity and timing of application of electric and magnetic fields density distribution of “fuel” (D and T)