000			03254nam a22004218i 4500
001			CR9781139017329
005			20250919142037.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110215s2014||||enk o ||1 0|eng|d
020			_a9781139017329 (ebook)
020			_z9780521899901 (hardback)
020			_z9780521728522 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA274.25 _b.L67 2014
082	0	0	_a519.2/2 _223
100	1		_aLord, Gabriel J., _eauthor.
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.
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (xi, 503 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aCambridge texts in applied mathematics ; _v50
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
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			_aThis 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 modelling and materials science.
650		0	_aStochastic partial differential equations.
700	1		_aPowell, Catherine E., _eauthor.
700	1		_aShardlow, Tony, _eauthor.
776	0	8	_iPrint version: _z9780521899901
830		0	_aCambridge texts in applied mathematics ; _v50.
856	4	0	_uhttps://doi.org/10.1017/CBO9781139017329
907			_a.b16843253 _b2020-12-22 _c2020-12-22
942			_n0
998			_a1 _b2020-12-22 _cm _da _feng _genk _y0 _z.b16843253
999			_c651669 _d651669