Youssef Marzouk Massachusetts Institute of Technology. joint work with Xun Huan (MIT), Habib Najm (Sandia), Dongbin Xiu (Purdue)


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1 Youssef Marzouk Massachusetts Institute of Technology joint work with Xun Huan (MIT), Habib Najm (Sandia), Dongbin Xiu (Purdue)
2 Questions How to quantify confidence in computational predictions? Use these predictions in design and decisionmaking. Uncertainties abound: parameters, inputs, boundary conditions, model structure How to build models? Learning from noisy, indirect experimental observations. How to choose observations? What best to measure? Need tractable methodologies for complex physical models!
3 A few applications reacting flow, chemical kinetics chemical models in reacting flow atmospheric transport and dispersion groundwater flow, contaminant remediation, carbon sequestration
4 Statistical inference Building and refining models a Bayesian approach Model parameters (or structure) m are random variables Apply Bayes rule: posterior density p( m d) = ( ) p m ( ) p m p d m p d m π m likelihood function L(m) ( ) ( )dm ( ) ( ) p m d prior density The posterior density is the full Bayesian solution to the inference problem Not just a single value for m, but a probability density A complete description of uncertainty An input to future simulations for prediction with uncertainty
5 Computational challenge Extracting information from the posterior Means, variances, higher moments; marginal distributions; realizations; posterior predictions But Posterior evaluations are expensive (forward model = PDE) Posteriors are often highdimensional Key step: Use stochastic spectral methods in Bayesian inference Enable statistical inference with more realistic physical models (via multiple orderofmagnitude speedups)
6 Things on my poster 1 Accelerating Bayesian inference with stochastic spectral methods 2 Spatially distributed parameters; inference in high dimensions 3 Optimal experimental design
7 Stochastic spectral methods Tool for efficient uncertainty propagation and assessment Polynomial chaos [Wiener 1938, Kakutani 1961, Ghanem 1991, ] Random variable X : Ω R with finite variance PCe: { ( )} i=1 { ( )} ( ) X(ω) = x k Ψ k ξ i1,ξ i2, k =0 ξ i ω are i.i.d. random variables (e.g., Gaussian) Ψ k ξ are orthogonal multivariate polynomials (e.g., Hermite) Polynomials of random variables; numerous extensions (piecewise polynomials, wavelets, etc)
8 Accelerating Bayesian inference Propagate prior uncertainty through the forward problem forward ( ) G j ( ξ ) = g jk Ψ k ( ξ ) m ξ model Galerkin or collocation (many methods here ) Result: a stochastic spectral representation of forward model predictions (compute once) Use this as a surrogate for the forward model in the likelihood function. No repeated forward problem solutions! P k= 0 [Marzouk et al JCP 2007]
9 Convergence result Simplest surrogate posterior density (ξ m) Convergence of the forward approximation implies convergence of the posterior distribution: Assume observational error is i.i.d. Gaussian, is prior density on ξ If π N ξ n d ( ) = p η ( d i G N,i (ξ)) p ξ ( ξ ) i=1 G i ( ξ ) G N,i ( ξ ) L 2 p ξ D( π N π ) N α CN α, 1 i n d, α > 0 then for sufficiently large N. p ξ [Marzouk & Xiu, CiCP 2009]
10 Speedup Total computational time vs number of posterior samples Persample cost reduced by 3 4 orders of magnitude!
11 Example: kinetic parameters Example: estimate kinetic parameters in a genetic toggle switch DAE model from [Gardner et al 2000] Real experimental data: steadystate expression levels of one gene (v) du = α 1 dt 1 + v u β dv = α 2 dt 1 + w v γ u w = ( 1 + [IPTG] K ) η
12 Example: kinetic parameters 1parameter and 2parameter marginal posterior densities gpcbased pseudospectral approach (dim = 6, N = 4, 6level sparse grid)
13 Distributed parameters Characterize spatially distributed parameters and states from sparse and indirect data Typical/realistic problems are illposed and highdimensional Example: flow in porous media M(x) = log ν(x) is an unknown logpermeability; infer from a few sources/sensors in the domain D = [0,1] Bayesian approach: u t = ( ν(x) u( x,t) ) + s( x,t) M(x,ω) is a stochastic process Inference and prediction using p(m d) M(x) sources sensors
14 Bayesian inverse problems Big picture of the computational approach: Endow the field M(x,ω) with an appropriate prior and hyperpriors Spectral methods propagate prior uncertainty through the forward model Efficient MCMC simulation from the surrogate posterior M(x,ω) forward model P G j ( ξ) = g jk Ψ k ξ k =0 ( ) Key steps: reduce dimensionality and exploit illposedness
15 Gaussian process priors Examples of stationary Gaussian random fields: C( x 1, x 2 ) = θ exp( x 1 x 2 L) C x 1, x 2 (need many KL modes) ( 2L 2 ) ( ) = θ exp x 1 x 2 2 (fewer KL modes) M(x,ω) = µ(x) + K i=1 λ i c i (ω) φ i (x)
16 Bayesian inversion Results: posterior distribution of logpermeability profile Dimensionality reduction (KL expansion) based on prior information MCMC sampling from the surrogate posterior (200K samples) mean, stdev, realizations median, quantiles, true profile [Marzouk & Najm 2009]
17 Bayesian inversion Results: posterior distribution of logpermeability profile Dimensionality reduction (KL expansion) based on prior information MCMC sampling from the surrogate posterior (200K samples) gridbased, direct forward evals s KarhunenLoève basis + poly chaos 248 s
18 Structure of the posterior 1D & 2D posterior marginals of the KL mode strengths, p(c i d): Rougher modes revert to the prior distribution! ( ) p( c i θ) p c m,c m+1, d,θ i m
19 Structure of the posterior Posterior distributions p(c i d,θ) with coarser data: c 6, 13 sensors c 6, 2 sensors ( ) p( c i θ ) p c m,c m +1, d,θ i m at smaller m Additional opportunities for dimensionality reduction
20 Dimensionality reduction How big/complex is an inference problem? The dimensionality of the inference problem should also reflect the data resolution and the forward physics A compromise between information in the prior and information in the likelihood function Example: linear forward model, Gaussian prior + noise y = Gx + η η ~ N ( 0,Γ obs ), x ~ N ( 0,Γ ) pr H G T Γ obs 1 G x y N ( µ post,γ ) post is the Hessian of loglikelihood
21 Dimensionality reduction Linear example (continued): Consider generalized Rayleigh quotient Information about w from the likelihood How much does the prior constrain w? w T Hw w T Γ 1 pr w Hw = λγ pr 1 w Generalized eigenproblem yields lowrank approximation of the change to posterior covariance: Γ post Γ pr W k Σ k W k T, with Σ k = Approximate the forward model only in directions where the posterior is most different from the prior (i.e., range of W k ) Equivalently: seek conditional independence ( Λ 1 k + I ) 1 k
22 Things on my poster 1 Accelerating Bayesian inference with stochastic spectral methods 2 Spatially distributed parameters; inference in high dimensions 3 Optimal experimental design
23 Experimental design We can infer parameters from indirect and imperfect data, and we can propagate the resulting uncertainties to outputs of interest. What about optimal experimental design? What to measure? When/where to do it? What experimental conditions yield the most information? Forward models are nonlinear; need simulationbased approaches Broad applications: structural health monitoring; well placement; sensor steering; testing/characterization of alternative fuels, electrochemical devices, catalytic surfaces
24 Experimental design Combustion example: ignition with detailed chemistry (H 2 O 2 ) Infer kinetic parameters (preexponentials, activation energies) from measurements of ignition delay, peak heat release rate, peak radical concentrations. What initial mixture conditions (T, φ, p, dilution) should one choose? T = 1000 K, φ = 1.0 T = 1000 K, φ = 1.0 T = 2000 K, φ = 5.0
25 Experimental design Combustion example: ignition with detailed chemistry (H 2 O 2 ) Infer kinetic parameters (preexponentials, activation energies) from measurements of ignition delay, peak heat release rate, peak radical concentrations. What initial mixture conditions (T, φ, p, dilution) should one choose? T = 1000 K, φ = 1.0 T = 1000 K, φ = 1.0 T = 900 K, φ = 0.1
26 Experimental design Choose experimental conditions via Bayesian optimal design Expected utility of a design η: U ( η) = U ( θ,η, D) p( D,θ η) dθ dd data D parameters θ Maximize this with respect to η (need to simultaneously integrate and maximize) Good choice for parameter inference: expected information gain (entropy of posterior relative to prior) ( ) = log p θ D,η p( θ) U η ( ) p( D,θ η) dθ dd gpc surrogates and efficient numerical integration make simulationbased optimal design feasible
27 Experimental design Contours of expected information gain in (A, E), with two design variables (T, φ)
28 Summary & thanks Bayesian inference and prediction Stochastic spectral methods to accelerate Bayesian inference From forward UQ to inverse problems Point parameters, spatially distributed parameters (dimensionality) Optimal Bayesian experimental design Support from DOE Office of Advanced Scientific Computing Research (ASCR), Sandia National Laboratories, and KAUST
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