000			03237nam a2200421 i 4500
001			74523
003			BD-RjUL
005			20211209082550.0
008			181014t20142014nyu b 001 0 eng
020			_a9780521899901 (hardback)
020			_a0521899907 (hardback)
020			_a9780521728522 (paperback)
020			_a0521728525 (paperback)
035			_a(BD-RjUL)
040			_aDLC _beng _cDLC _erda _dDLC _dBD-RjUL
041			_aeng
042			_apcc
082	0	0	_a519.22 _223 _bLOI 2014
100	1		_aLord, Gabriel J., _eauthor. _9223740
245	1	3	_aAn introduction to computational stochastic PDEs / _cGabriel J. Lord, Heriot-Watt University, Edinburgh, Catherine E. Powell, University of Manchester, Tony Shardlow, University of Bath.
246	3		_aIntroduction to computational stochastic partial differential equations
264		1	_aNew York, NY, USA : _bCambridge University Press, _c2014.
300			_axi, 503 pages : _billustrations (some color) ; _c26 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	0		_aCambridge texts in applied mathematics ; _v50
504			_aIncludes bibliographical references (pages 489-498) and index.
505	8		_aMachine generated contents note: Part I. Deterministic Differential Equations: 1. Linear analysis; 2. Galerkin approximation and finite elements; 3. Time-dependent differential equations; Part II. Stochastic Processes and Random Fields: 4. Probability theory; 5. Stochastic processes; 6. Stationary Gaussian processes; 7. Random fields; Part III. Stochastic Differential Equations: 8. Stochastic ordinary differential equations (SODEs); 9. Elliptic PDEs with random data; 10. Semilinear stochastic PDEs.
520			_a"This book gives a comprehensive introduction to numerical methods and analysis of stochastic processes, random fields and stochastic differential equations, and offers graduate students and researchers powerful tools for understanding uncertainty quantification for risk analysis. Coverage includes traditional stochastic ODEs with white noise forcing, strong and weak approximation, and the multi-level Monte Carlo method. Later chapters apply the theory of random fields to the numerical solution of elliptic PDEs with correlated random data, discuss the Monte Carlo method, and introduce stochastic Galerkin finite-element methods. Finally, stochastic parabolic PDEs are developed. Assuming little previous exposure to probability and statistics, theory is developed in tandem with state-of the art computational methods through worked examples, exercises, theorems and proofs. The set of MATLAB codes included (and downloadable) allows readers to perform computations themselves and solve the test problems discussed. Practical examples are drawn from finance, mathematical biology, neuroscience, fluid flow modeling and materials science"--
650		0	_aStochastic partial differential equations. _9223741
650		7	_aMATHEMATICS / Differential Equations. _2bisacsh _9223742
700	1		_aPowell, Catherine E., _9223745
700	1		_aShardlow, Tony, _9223744
942			_2ddc _cBK
999			_c74523 _d74523